1

ÇUKUROVA UNIVERSITY

THE INSTITUTE OF SOCIAL SCIENCES

DEPARTMENT OF ENGLISH LANGUAGE TEACHING

LEXICAL COLLOCATIONS (Verb + Noun) ACROSS WRITTEN ACADEMIC

GENRES IN ENGLISH

Eser ÖRDEM

A PhD DISSERTATION

ADANA, 2013

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ÇUKUROVA UNIVERSITY

THE INSTITUTE OF SOCIAL SCIENCES

DEPARTMENT OF ENGLISH LANGUAGE TEACHING

LEXICAL COLLOCATIONS (Verb + Noun) ACROSS WRITTEN ACADEMIC

GENRES IN ENGLISH

Eser ÖRDEM

Supervisor: Prof. Dr. Erdoğan BADA

A PhD DISSERTATION

ADANA, 2013

3

To Directorate of the Institute of Social Sciences of Cukurova University,

We certify that this dissertation is satisfactory for the award of degree of Doctor of Philosophy in the subject of English Language Teaching.

Chairperson : Prof. Dr. Erdoğan BADA Supervisor Member of Examining Committee : Prof. Dr. Yasemin BAYYURT Member of Examining Committee : Assoc. Prof. Dr. Ahmet DOĞANAY Member of Examining Committee : Asst. Prof. Dr. Turan PAKER Member of Examining Committee : Asst. Prof. Dr. Hasan BEDİR

I certify that this dissertation conforms to the formal standards of the Institute of Social Sciences. ……/……/…….

Prof. Dr. Azmi YALÇIN Director of the Institute

PS. The uncited usage of the reports, charts, figures, and photographs in this dissertation, whether original or quotes for mother sources, is subject to the Law of Works of Art and Thought No: 5846.

Not: Bu tezde kullanılan özgün ve ba ka kaynaktan yap ılan bildiri lerin, çizelge, ekil ve fotoğrafların

kaynak gösterilmeden kullan ımı 5846 sayılı Fikir ve Sanat Eserleri Kanunu’ndaki hükümlere tabidir.

iii

ÖZET

İNGİLİZCEDE YAZILI AKADEMİK TÜRLERDE SÖZCÜK

EŞDİZİMLİLİKLERİ

Eser ÖRDEM

Doktora tezi, İngiliz Dili Eğitim Anabilim Dalı

Danışman: Prof.Dr. Erdoğan BADA

Mayıs, 2013, 158 Sayfa

Dilbilimsel çalışmaların uzun süre sözdizimi ağırlıklı olması, sözcük ve

dilbilgisinin iki ayrı kategori olarak algılanmasına neden olmuştur. Bilişsel dilbilim

paradigmasıyla birlikte sözdizimi çalışmalarının yerini sözcük ve kavramlar almıştır. Bu

alandaki çalışmaların katkısıyla anlam ön plana çıkarılmıştır ve sözdiziminden sözcüğe

doğru bir yönelim olmuştur. Konstrüksiyon (oluşum) dilbilgisinin katkısıyla da sözdizimi

ve sözcük arasındaki süreklilik vurgulanmıştır. Öte yandan, derlem dilbilimin ortaya

çıkmasıyla hem bu sözcük-dilbilgisi arasındaki sürekliliği vurgulayan çalışmalar hız

kazanmış hem de eş dizimlilik çalışmaları artmıştır. Böylelikle, eş dizimlilik çalışmaları

dilbilimde kuramsallaştırılmıştır. Eş dizimlilik çalışmaları başlangıçta sadece sözcük

düzeyinde iken, süreçte sözcükler dilbilgisini de içerisine almıştır. Bu görüşe göre, her

sözcüğün kendine ait dilbilgisel özellikleri vardır. Böylece sözcük ve dilbilgisi birbirinden

farklı inceleme alanları olarak değerlendirilmemelidir. Çünkü belirtildiği gibi, sözcük ve

dilbilgisi arasındaki ilişki süreksiz değil süreklidir. Bu paradigma çerçevesinde, bu

çalışmada sağlık, fen ve sosyal bilimlerde kullanılan fiillerin ortak ve farklı özellikleri

ortaya konulmaya çalışılmıştır. Bunun için derlem temelli verilerden yola çıkılmış,

İngilizcede akademik türlerdeki fiil+isim eş dizimlilikleri ve sıklıkları ki-kare analizi ile

verilmiştir. Sonuçlar, sağlık ile fen bilimlerinin fiil+isim eş dizimliliğini kullanmada daha

çok ortak özellik gösterirken, sosyal bilimlerin fiil+isim eş dizimliliğini kullanma

bakımından diğer iki akademik türden farklılık olduğunu göstermiştir.

Çalışmayla her üç akademik türde ortak 165 fiil bulunmuştur ve prototip sözcük

adayı olarak da 165 fiilden sıklığına göre 12 fiil+isim eş dizimliliği tespit edilmiştir.

Anahtar sözcükler: Derlem dilbilim, eş dizimlilik, yapı (oluş), yazılı akademik tür

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ABSTRACT

LEXICAL COLLOCATIONS (Verb + Noun) ACROSS WRITTEN ACADEMIC

GENRES IN ENGLISH

Eser ÖRDEM

PhD Dissertation, English Language Teaching Department

Supervisor : Prof.Dr.Erdoğan BADA

May, 2013, 158 Pages

The dominance of syntactic studies in linguistics has caused lexis and grammar to

be perceived as two distinct categories. With introduction of the paradigm of cognitive

linguistics, the studies in syntax have been replaced by those in lexis and concepts.

Semantics has come to the fore through the studies in cognitive linguistics, and there has

been a trend from syntactic studies to lexical ones. In addition to research in cognitive

linguistics, construction grammar has also emphasized the continuum between lexis and

grammar. With the emergence of corpus linguistics, the studies regarding the continuum

between lexis and grammar have gained momentum, and thus studies of collocations have

been theorized. Early studies of collocations have focused on only lexis and disregarded

grammar. However, in the process the studies have also incorporated grammar as well, and

this view supports the idea that each word has its own grammatical properties. Therefore,

lexis and grammar should be studied on the same continuum because there is a continuum

between these two categories rather than a discontinuum. Within the framework of this

paradigm, this study focused on verb+noun lexical collocations across the health, physical

and social sciences in the written academic genre and analyzed these lexical collocations

through the frequency and chi-square analysis. The study aimed to search for commonalities

and differences between the verbs with their collocations. The results showed that there

were more similarities and relationship between the health and physical sciences, while the

social sciences indicated a significant difference compared to the other two. The study

found 165 common verbs used across the three sciences. 12 verbs among the 165 verbs

were found to be candidates verb+noun lexical collocations as prototypes.

Key words: Corpus linguistics, collocations, construction, prototype, written academic

genre

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ACKNOWLEDGEMENTS

So many people were involved in this study in the process and supported me

considerably. I would like to thank my supervisor, Prof. Dr. Erdoğan BADA without

whose encouragement this product would not be finished. Describing him is beyond the

words. One may have difficulty describing and expressing love in everyday life. My

affair with Dr. BADA is based on excitement in science and life. My deep thanks also

go to Prof.Dr. Hatice Sofu. Since I met her in the class during my undergraduate years,

she supported me with her motivating words. I also thank Associate Prof. Dr. Ahmet

DOĞANAY whose feedback was important to me while doing the statistics. However,

Dr. Doğanay is beyond the statistics. He is more social and human in this statistical

personality. I also thank Prof. Dr. Yasemin Bayyurt for her feedback during the jury

study. Although I spent a few days with her, I noticed the deep and unlost sensitivity in

her to nature, animals and humans.

One of the cornerstones in my life is just before me now and supports me with

all his energy and constructive feedback. I have learned a lot from him. Even when I

have not contacted with him for weeks, I have always felt his support. He has created a

great change in my life. This great person is Assistant Prof. Dr. Turan Paker. I miss him

playing his Saz. My special thanks also go to Assistant Prof. Dr. Hasan Bedir who has

always been a very good teacher and friend to me. Since 1993, he has supported and

trusted me. He has always welcomed me into his office to discuss various issues that

have always excited me. My thanks also go to Assistant Prof. Dr. Bilal Genç who gave

me feedback during this process. I also thank Assistant Prof. Dr. Rana YILDIRIM

because whenever I see her, she just gives me positive energy with her constructive

feedback. I also have to mention one of the greatest people in my life, Associate Prof.

Dr. Muna Yüceol Özezen who has greatly changed my life and with whom I have

often made academic discussions. I have always admired her prolific lifestyle and

sensitivity to any issue in the world. She and her family have always been great people

and friends in my life. I also thank İsmail Sanberk who encouraged me to finish this

dissertation so that we could start other studies. He has been a great motivator during

this process. Can Meşe, a great friend, has always supported me while writing this

dissertation. He always helped me design the study in a better way. One of my best

friends, Yusuf Karahan, supported me psychologically and gave the necessary

incentives to stimulate me. I have never seen or met a wonderful person like Yusuf.

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Gaffar Terzi, a great artist and a great drama reporter, actor and leader, and her fresh

and new flowerish girlfriend Pınar have always made me feel their warmth. They are

real philanthropists and protagonists of this world. There is another name I need to

mention. Assistant Prof. Dr. Ömer Tuğrul Kara entered my life very recently but

changed the course of my life in a short time with his motivating words and helped me

a lot during this process. I also have to thank Tahir Tahiroğlu, who helped me learn

more about corpus and whom I have often had the chance to discuss linguistic issues

with. Ali Bekem and Özgür Mahir Ekinci, my fantastic and Carpe-Diem friends,

supported me generously during this process. Their non-hesistant sharing desire

motivated me to continue my study. There are also in life people whose names are kept

secret and who do not want to be mentioned because of their modest nature but support

you all the time. I also thank them a lot.

I would like to express my deepest gratitude to my wife, Özlem Ördem

Aydoğmuş without whose support I could not achieve my goal. She has always been a

great company during this process. She has always showed an effort a woman is almost

impossible to make and has always prepared a convenient setting for me day and night

so that I can continue my studies in a more comfortable way.

My mom was beside me in each step of this dissertation. She is more than a

mother. She felt this excitement as much as I did. I also send my thanks to my family

that I will ever miss even when I am with them.

vii

TABLE OF CONTENTS

ÖZET ................................................................................................................................. iii

ABSTRACT ....................................................................................................................... v

ACKNOWLEDGEMENTS ............................................................................................. vii

CONTENTS ...................................................................................................................... ix

LIST OF TABLES ............................................................................................................ xiv

LIST OF FIGURES .......................................................................................................... xii

LIST OF APPENDICES .................................................................................................. xvi

CHAPTER 1

INTRODUCTION

1.0. Introduction .................................................................................................................. 1

1.1. Background of the Study ............................................................................................. 2

1.2. Statement of the Problem ............................................................................................. 2

1.3. The Aim of the Study ................................................................................................... 3

1.4. Research Questions ...................................................................................................... 3

1.5. Operational Definitions and Key Terms ...................................................................... 4

1.6. Assumptions and Limitations. ..................................................................................... 5

CHAPTER 2

REVIEW OF LITERATURE

2.0. Introduction .................................................................................................................. 6

2.1. Traditional Understanding of the Lexicon and Grammar ............................................ 6

2.2. Formulaic Nature of Language and the Lexicon ......................................................... 7

2.3. Theoretical Backround to Collocations ....................................................................... 8

2.4. Approaches to Collocations ......................................................................................... 8

2.4.1. Psychological Explanation ................................................................................. 9

2.4.2. Firthian and Neo-Firthian Approaches .............................................................. 12

2.4.3. Psychological Approaches to Collocations ........................................................ 13

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2.4.4. Lexical Priming Approach to Collocations ....................................................... 14

2.5. Construction Grammar ................................................................................................ 16

2.5.1. Collostructions Analysis .................................................................................... 18

2.6. Corpus Linguistics ....................................................................................................... 18

2.7. Written Academic Disciplines ..................................................................................... 21

CHAPTER 3

METHODOLOGY

3.0. Introduction .................................................................................................................. 23

3.1. Research Design .......................................................................................................... 23

3.2. Data Gathering Procedure ............................................................................................ 24

3.2.1. Written Academic Corpora ............................................................................... 24

3.3. Empirical Methods in Corpus Linguistics ................................................................... 26

3.3.1. Descriptive Statistics ......................................................................................... 26

3.3.2. Analytical Statistics .......................................................................................... 27

3.4. Data Processing and Analysis Procedures ................................................................... 27

3.5. Software Programs ....................................................................................................... 27

3.6. Coding of verb+noun Collocations ............................................................................. 28

CHAPTER 4

RESULTS AND DISCUSSION

4.0. Introduction .................................................................................................................. 30

4.1. Overall Results of the Study ........................................................................................ 30

4.2. Results Related to Research Questions (1) .................................................................. 33

4.2.1. Verbs and Collocates in the Health Sciences ..................................................... 33

4.2.2. Verbs and Collocates in Physical Science Genre .............................................. 60

4.2.3. Verbs and Collocates in the Social Sciences ..................................................... 83

4.2.4. Comparison of the Results ................................................................................. 111

4.3. Results Related to Research Questions (2) .................................................................. 119

4.3.1. Collostructions in the Health Science Genre ..................................................... 120

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4.3.2. Collostructions in the Physical Sciences ........................................................... 121

4.3.3. Collostructions in the Social Sciences ............................................................... 123

4.4. Results Related to Research Question (3) .................................................................... 124

CHAPTER 5

CONCLUSION

5.0. Introduction .................................................................................................................. 127

5.1. Summary of the Study ................................................................................................. 127

5.2. Implications and Recommendations for Language Learning and Teaching ............... 128

5.3.. Limitations of the Study ............................................................................................. 131

5.4. Future Research .......................................................................................................... 131

REFERENCES .................................................................................................................. 133

APPENDICES ................................................................................................................... 140

CURRICULUM VITAE................................................................................................... 157

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LIST OF TABLES

Pages

Table 1: Research Type and Stages of the Study ........................................................... 24

Table 2: The Overall Data of the Texts ......................................................................... 25

Table 3: Disciplines Chosen for the Corpora in Three Distinct Sciences ..................... 25

Table 4: Descriptive Statistis of Verbs According to the Tokens .................................. 32

Table 5: The Overall Statistical Results of Verbs According to the Types ................... 32

Table 6: The Frequency and Percentages of Verbs in the Health Sciences ................... 33

Table 7: Frequency and Percentages of find Collocates ................................................ 37

Table 8: Frequency and Percentages of show Collocates .............................................. 41

Table 9: Frequency and Percentages of suggest Collocates .......................................... 44

Table 10: Frequency and Percentages of indicate Collocates ........................................... 48

Table 11: Frequency and Percentages of determine Collocates ....................................... 52

Table 12: Frequency and Percentages of provide Collocates ............................................ 56

Table 13: Frequency and Percentages of Verbs in the Physical Sciences ....................... 61

Table 14: Frequency and Percentages of find Collocates .................................................. 64

Table 15: Frequency and Percentages of show Collocates ............................................. 68

Table 16: Frequency and Percentages of provide Collocates .......................................... 72

Table 17: Frequency and Percentages of determine Collocates ..................................... 77

Table 18: Frequency and Percentages of indicate Collocates ......................................... 81

Table 19: Frequency and Percentages of Verbs in the Social Sciences ........................... 84

Table 20: Frequency and Percentages of find Collocates ................................................. 87

Table 21: Frequency and Percentages of understand Collocates ....................................... 93

Table 22: Frequency and Percentages of make Collostrucs and Collocates .................... 98

Table 23: Frequency and Percentages of provide Collocates ...................................... 104

Table 24: Frequency and Percentages of suggest Collostructions and Collocates ...... 109

Table 25: Descriptive Statistics of Common Verbs Across Three Genre ..................... 112

Table 26: Overall Results of the Chi-Square Analysis the Common Verbs in the Three

Academic Genres ......................................................................................... 112

Table 27: Descriptive Statistics of Each Common Verb ............................................... 113

Table 28 Percentages of Common Verbs in the Health and Physical Sciences ........... 118

Table 29: Results of the Chi-Square Analysis the Common Verbs in the Health and

Physical Sciences ......................................................................................................... 119

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Table 30: Distribution of Most Five Frequent Verbs in the Health Sciences .............. 121

Table 31: Frequency and Percentages of Collostructs in the Health Sciences ............ 121

Table 32: Distribution of Most Frequent Verbs in the Physical Sciences ................... 122

Table 33: Distribution of Collostructs of Most Frequent Verbs in the Physical

Sciences ....................................................................................................... 122

Table 34: Distribution of Most Frequent Verbs in the Social Sciences ....................... 123

Table 35: Distribution of Collostructs of Most Frequent Verbs in the Social

Sciences ............................................................................................................ 123

Table 36: Distribution of the Collostruct of the Verb make in the Social Sciences .... 124

Table 37: Chi-Square Analysis of Most Common Verbs across the Three Genres ..... 125

xii

LIST OF FIGURES

Pages

Figure 4.1. Summary Statistics of the Health Science Texts .......................................... 30

Figure 4.2. Summary Statistics of the Physical Science Texts ....................................... 31

Figure 4.3. Summary Statistics of the Social Sciences Texts ......................................... 31

Figure 4.4. Frequency of find Collocates ....................................................................... 38

Figure 4.5. Concordance Lines of find Collocates ......................................................... 39

Figure 4.6. Frequency of show Collocates ...................................................................... 42

Figure 4.7. Concordance Lines of show Collocates ........................................................ 42

Figure 4.8. Frequency of suggest Collocates .................................................................. 45

Figure 4.9. Concordance Lines of suggest Collocates .................................................... 46

Figure 4.10. Frequency of indicate Collocates ............................................................... 49

Figure 4.11. Concordance Lines of indicate Collocates ................................................. 50

Figure 4.12. Frequency of determine Collocates ............................................................ 53

Figure 4.13. Concordance Lines of determine Collocates .............................................. 54

Figure 4.14. Frequency of provide Collocates ................................................................ 57

Figure 4.15. Concordance Lines of provide Collocates .................................................. 58

Figure 4.16. Frequency of the Verb find Collocates ....................................................... 66

Figure 4.17. Concordance Lines of find Collocates ........................................................ 66

Figure 4.18. Frequency of show Collocates .................................................................... 69

Figure 4.19. Concordance Lines of show Collocates ...................................................... 70

Figure 4.20. Frequency of provide Collocates ................................................................ 74

Figure 4.21. Concordance Lines of provide Colocates ................................................... 74

Figure 4.22. Frequency of determine Collocates ............................................................ 78

Figure 4.23. Concordance Lines of determine Collocates .............................................. 79

Figure 4.24. Frequency of indicate Collocates ............................................................... 82

Figure 4.25. Concordance Lines of indicate Collocates ................................................. 82

Figure 4.26. Frequency of find Collocates ...................................................................... 89

Figure 4.27. Concordance Lines of find Collocates ........................................................ 90

Figure 4.28. Frequency of understand Collocates .......................................................... 95

Figure 4.29. Concordance Lines of understand Collocates ............................................ 95

Figure 4.30. Frequency of make Collocates 100

xiii

Figure 4.31. Concordance Lines of make Collocates 101

Figure 4.32. Frequency of provide Collocates 106

Figure 4.33. Concordance Lines of provide Collocates 107

Figure 4.34. Frequency of suggest Collocates 110

Figure 4.35. Concordance Lines of provide Collocates 110

xiv

LIST OF APPENDICES

Pages

Appendix 1 List of Verbs in the Health Sciences ........................................................ 140

Appendix 2 List of Verbs in the Physical Sciences .................................................... 146

Appendix 3 List of Verbs in the Social Sciences ......................................................... 150

1

CHAPTER 1

INTRODUCTION

1.0. Introduction

New theories of formulaic language and lexicon have been prevalent with the

contributions of construction grammar and corpus linguistics. Shifting from generative

grammar to formulaic language has altered perspectives pertinent to domains of

language (Wray, 2002). It has been often emphasized that language, whether spoken or

written, is composed of prefabricated routines and fixed expressions. One of the

subcategories of formulaic language is collocations, mainly made up of grammatical

and lexical units. Howarth (1998) classifies collocations as lexical and grammatical

units and explicates that ‘‘lexical collocations consist of two open class words (verb +

noun, adjective +noun), while collocations between one open and one closed word are

grammatical’’ (p.27). The studies of lexical collocations in particular have been prolific

in recent decades resulting in approaching even the term ‘collocation’ from different

perspectives and distinct definitions. However, it is still one of the most controversial

topics in linguistics although it is often defined as ‘a relationship between lexical items

that regularly co-occur’ (Carter, 1998, p.163). Even early linguists such as Saussere

(1916), Bloomfield (1933) and Firth (1951) recognized and dwelt upon the importance

of collocations with similar approaches and definitions. In the same way, formulaic

aspect of collocations was emphasized by other linguists as well (Hymes, 1962;

Bolinger, 1976; Fillmore, 1979).

Subsequent to the diagnosis of importance of collocations, computational

lexicographers (Sinclair, 1991, 1996) have empirically used collocations in their studies.

These kinds of applications have led to the emergence of corpus based collocation

dictionaries (Sinclair, 2004, 2005). However, it still remains a problem to determine

which two words regularly co-occur in a text since one can encounter different kinds of

collocations at different levels. It is quite important to make distinctions between

collocations and apply the right statistical analysis while extracting collocations. Since

different researchers have reached different conclusions even about the collocations of

the same word, a closer look at the nature of collocations through the help of corpus

linguistics is highly needed.

2

1.1. Background of the Study

The realization of formulaic language, specifically collocations, can be traced

back to pre-Chomskyan period (Saussere, 1916; Firth, 1951). However, the prevalence

of syntactic studies downgraded the importance of lexis. Since computational linguistic

and corpora studies were still immature, the importance of lexis and formulaic language

had been long ignored. Collocation studies have been resuscitated with the emergence

of corpora studies inasmuch as they provide a large amount of data empirically (Evert,

2007). In the last few decades studies of collocations have advanced in four main

courses. Firstly, Firthian perspective of collocations refers to the predictability of co-

occurrences with the mutual expectancy of collocations (Evert, 2007). As Firth (1957)

puts it, ‘‘you shall know a word by the company it keeps’’ (p.179). Sinclair (1991,

1995) also depicts collocations as a combinaton of two words that are biased to act

together using the term in Firthian sense. Sinclair (1991) refers to collocation as “the

occurrence of two or more words within a short space of each other in a text”(p.170). In

this sense, Sinclair’s studies are often referred to as Neo-Firthian (Evert, 2007).

Secondly, the term ‘collocation’ has also been used as phraseological or semi-

compositional (Cowie, 1981, 1994; Hausmann, 1989). In the phraseological approach,

collocations are syntactically related. Thirdly, Hoey (2005) regards collocations as

psychological associations between words. Lastly, an eclectic view related to

collocations is shared by Bartsch (2004) who describes collocations as “lexically and/or

pragmatically constrained recurrent co-occurrences of at least two lexical items which

are in a direct syntactic relation with each other” (p.76).

Although collocation studies are on the dramatic increase, Hoey (2005) still

views definitions and properties of collocations as vague and rather complex, and

therefore adds that ‘‘lexis is complexly and systematically structured and that grammar

is an outcome of this lexical structure’’ (p.1). New perspectives to collocations studies

still remain to be developed.

1.2. Statement of the Problem

Since the term ‘collocation’ covers a very heterogeneous collection of

combinations of lexical items, it is important to produce alternatives and solutions to

this problem. Another problem with collocations is that this is taken into consideration

3

without the analysis of current linguistic theories. The previous research did not focus

on lexical collocations from the perspective of certain linguistic theories. Rather, those

studies viewed collocations as only a subpart of vocabulary. In the current studies

except a few, collocations are still, to a large extent, related to only lexis. Grammar and

lexis were not handled on the same continuum in language studies for a long time. It is

only in recent times that studies on grammar and lexis have begun to appear. Therefore,

it seems quite crucial to approach the issue under the umbrella of a certain linguistic

theory. As most of the studies are based on frequency effect, it is also necessary to

employ other statistical tools in order to be able to reach sound results.

1.3. The Aim of the Study

The main aim of this study is to investigate verb+noun lexical collocations

across written academic genres under the theory of construction grammar and

collostruction analysis by using corpora. We aimed to use Evert’s (2007) empirical

definition of collocations and statistical analysis in order to extract verb+noun

collocations, and Gries’ analysis (2012) of collostructions. Another aim of this was to

analyze lexical collocations closely with the analysis of construction grammar. By

applying construction grammar analysis, we intended to reach prototypes of some

certain lexical collocations and collostructs across the written academic genres. Thus,

we sought responses to the following questions.

1.4. Research Questions

This dissertation sought answers for the following questions:

1. What verb+ noun lexical collocations (collostructions) can be observed across

academic genres?

2. How are these lexical collocations (collostructions) constructed from a

constructionist grammar view?

3. Is it possible to discover prototypical lexical collocations (collostructions)

according to the academic genre?

The first question intends to seek an answer to the types of verb+noun

collocations across different written academic disciplines. The second question aims to

4

analyze these collocations from constructionist and collostructionist perspective. The

last question purports to find out whether prototypical lexical collocations can be

extracted and elicited from the distinct academic disciplines.

1.5. Operational Definitions and Key Terms

Since this study encompasses different disciplines composed of corpus

linguistics, constructionist grammar and cognitive linguistics, it deals with different

concepts that need to be defined.

Collocations: Lexically and/or pragmatically constrained recurrent co-occurrences of at

least two lexical items which are in a direct syntactic relation with each other (Bartsch,

2004; Evert, 2007).

Verb+noun collocations: A verb + noun collocation will not be restricted to only two

lexemes. The elements closely related to them will also be considered. For example,

take something into consideration will be considered a collocation. Combinations such

as posit an idea, posits an idea, posited an idea, the idea was posited will be handled as

occurrences of the same collocation. In case of any semantic change owing to the

morphological or movement variation in the combination, this difference will also be

considered.

Construction grammar: Constructions are generalizations over the restrictions on

lexical combinations (Croft & Cruse, 2004; Goldberg, 2006).

Collexeme : Lexemes that are attracted to a particular construction are referred to as

collexemes of this construction ( Stefanowitsch & Gries, 2003, 2013).

Collostruct: A construction associated with a particular lexeme may be referred to as

a collostruct ( Stefanowitsch & Gries, 2003, 2013).

Collostruction: Collostructions start with a particular construction and investigate

which lexemes are strongly attracted or repelled by a particular slot in the construction.

While collocations identify combinatorial preferences of lexical items irrespective of

the syntactic context and lack a syntactic dimension, collostructions take syntactic

context into consideration (Stefanowitsch & Gries, 2003). Therefore, the combination of

a collexeme and a collostruct will be referred to as a collostruction.

Prototype: The best or most representative member of a given category, concept or

lexeme involves some combination of related typical features. Meanings may cluster or

overlap due to the underlying semantic structures. Meaning is not equally distributed

5

among the constituents. Rather, summary representation and the best exemplars of a

lexeme form ideas about its meaning (Rosch, 1977).

1.6. Assumptions and Limitations

In corpus studies sampling and representativeness are commonly voiced

problems. These problems have engaged corpus linguists since the very beginning of

corpus studies. All instances from corpora were not represented in the study. Kilgariff,

Rundell &Ui Dhonnchadha (2006) noted that ‘‘ there are no generally agreed objective

criteria that can be applied to this task: at best corpus designers strive for a reasonable

representation of the full repertoire of available text types’’ (p.129). Therefore, the first

limitation of the study was sampling. Due to a limited a number of articles (249)

retrieved from three main disciplines), this procedure was carried out randomly. The

second limitation was to take only lexically-initiated verb+noun lexical collocations

across the three disciplines. Finally, we experienced difficulty in choosing a most

appropriate statistical tool from among 57 other measurement tools.

The next chapter will deal with the literature review by providing the theoretical

framework and approaches with their applications and findings.

6

CHAPTER 2

REVIEW OF LITERATURE

2.0. Introduction

This chapter discusses several approaches to the term ‘collocation’, aspects of

corpus linguistics and the theory of construction grammar. Firstly, four main approaches

to collocations are presented. After introducing the approaches, their strengths and

weaknesses are discussed. As an important means in the study, the aspects of corpus

linguistics are given. The emergence and contribution of this field to collocation studies

are emphasized. Lastly, the theoretical framework of construction grammar is

explained, and its tenets related to collocations are given.

2.1. Traditional Understanding of the Lexicon and Grammar

Grammar and lexicon have been conceptualized as separate for a long time since

Chomskyan notion of grammar has been dominant in linguistics in the last few decades

stating that grammar is primary and generative enabling words to be produced through

syntax (Hoey, 2005). Chomskyan understanding of language is based on the corollary

that a small number of linguistic universals and general grammatical rules with their

compositionality comprise language (Chomsky, 1995, 2000). In its traditional sense

Chomskyan view of language refers to the idea that grammar and lexicon are distinct

entities. (Chomsky, 2000). Certain principles and rules comprise grammar, while the

lexicon accommodates idiosyncratic information resulting in the fact that the lexicon

cannot be obtained from general principles of syntax (Chomsky, 1995). Language is

viewed as an autonomous cognitive faculty disregarding nonlinguistic cognitive abilities

(Croft & Cruse, 2004). Therefore, Chomskyan view of language prioritizes modularity

instead of psychological models or processes (Hoey, 2005; Croft & Cruse, 2004). The

importance of lexis is lessened inasmuch as syntax is regarded as the core device in

order to acquire language. It is conjectured that even semantics precedes lexis that is

acquired after the acquisition of semantics (Pinker, 1994). Lexis, formulaic language

and specifically collocations have been treated as peripheral, and formulaic language

does not provide any clues pertaining to language acquisition and language processing

7

(Pinker, 1994). Thus, there is a categorical division between grammar and the lexicon

in Chomskyan theory.

2.2. Formulaic Nature of Language and the Lexicon

Unlike generative grammar, the theory of formulaic language offers a holistic

approach and pragmatic understanding of language relying on prefabricated expressions

and chunks. Wray (2002) has amassed evidence and conducted studies in order to show

that grammar is an extension of lexis. Wray (2002) defines formulaic language as ‘‘a

sequence, continuous or discontinuous, of words or other elements, which is, or appears

to be, prefabricated: that is, stored and retrieved whole from memory at the time of use,

rather than being subject to generation or analysis by the language grammar’’ (p. 6).

Bloom (1973) argues that ‘‘language knowledge consists only of a set of

prefabricated phrases and sentences memorized from previous encounters with

them’’(p.17). In partial support of this idea, Wray (2002) states that humans embody

two systems which are both analytic and holistic. The former refers to analysis of

grammatical strings, and the latter is concerned with reduction of processing effort.

Methodologically it is impossible to rely on intuition. Therefore, a lot of formulaic

studies have been carried out on corpus research that strives to avoid using only

intuitive means. Wray (2002) accepts the weaknesses in methodology in searching for

formulaic language by hinging on the difficulty of comprehending what formulaic

language is and how it acts in a language. Lack of robust methodology is also stressed.

In terms of measurement of formulaic language, there is incredibly a vast difference

ranging from 4% to 80%. The problem of measuring formulaic sentences in terms of

percentage puts hard challenges before researchers. Only finding out how frequent a

word is used independently or in a collocational form is insufficient that can be

regarded as only raw frequency. It is also worth considering how often it could also

have occurred. The theory of formulaic language views collocations as pervasive in

language and relates collocations to the continuum of language because collocations

may range from free combinations to pure idioms. This study aims to keep track of this

knowledge not only in terms of syntax but also in terms of lexical collocations. It

intends to investigate how lexical collocations behave in abstract domains and looks at

the uses of such collocations.

8

2.3. Theoretical Background to Collocations

Different theories stressing the importance of lexis and lexical patterns in

languages harbor some characteristics of collocations. Cognitive linguistics (Croft &

Cruse, 2004; Evans & Green, 2006), cognitive grammar (Langacker, 1987,1991),

construction grammar (Fillmore, Kay & O’Connor,1988; Goldberg, 1995, 2006),

radical construction grammar (Croft, 2001), lexical-functional grammar (Bresnan,

1982), pattern grammar (Hunston & Francis, 2000), word grammar (Hudson, 1994),

formulaic language (Wray, 2002, 2008) and lexical priming (Hoey, 2005) have long

emphasized the importance of the continuum of lexis and grammar. Lexical priming

model holds the main ideas that have thoroughly theorized collocations in linguistics.

Cognitive grammar, although focusing on lexis, deals with grammar from a

psychological perspective (Langacker, 1987). This theory posits the idea that grammar

is meaning and basically semantic rather than syntactic. Each grammatical unit refers to

a sign in language. Correspondingly, cognitive linguistics takes both grammar and lexis

together while analyzing languages. Collocations are only a subfield for cognitive

linguistic researchers. Cognitive linguistics investigates languages through

psychological concepts and categories believing that language is embodied. Lexis has a

vital role in cognitive linguistics; lexis and grammar are closely associated with each

other.

2.4. Approaches to Collocations

The first evidence regarding the discovery of the notion of collocation dates

back to the 18th century by defining collocations as ‘an arrangement or ordering of

objects (esp. words) with reference to each other’ (Hoey, 2005,p. 3) and as ‘placing

things side by side’ (Durrant, 2008, p.5). In order to emphasize the importance of

formulaic language, studies from aphasic patients were shown as evidence. In this

sense, some findings dating back to the nineteenth century were obtained to indicate

that aphasic patients retained the ability of producing formulaic language fluently

although they could not generate new sentences (Wray, 2002). The studies of any kind

of aphasia has continued since the beginning of 1920s showing that formulaic language

contains different layers ranging from simple expressions to semi-fixed expressions

(Wray, 2002). This evidence points out that the historical background of collocations

9

has readied this term to be used in a richly contextualized way. Therefore, collocation is

a concept not only with several definitions but with different approaches as well.

2.4.1. Psychological Explanation

Psychology has only recently dealt with conceptual combinations dubbed as

lexical collocations in linguistics. Conceptual combinations account for the

psychological rationale of lexical combinations in a sense. Lexical collocations are not

only superficial expressions that are not analyzed conceptually. In contrast, lexical

collocations find some explanations in psychology in terms of how two words come and

act together meaningfully. Lexical collocations basically refer to concepts and

conceptual content in mind. Murphy (2004) states that semantic content entails

conceptual content. That is, if a word one knows means something, that something must

be part of one’s conceptual structure. Second, no semantic distinctions can be made that

are not distinguished in conceptual structure. One must perceive the differences

between two objects such as chair and stool represented in your concepts of furniture

(Murphy, 2004). There is also a social process of converging on meaning that is an

important aspect of language (Clark, 1996). Kelly, Bock and Keil (1986) found that

‘‘the sentence planning process was sensitive to the typicality of concepts, which in turn

influenced the accessibility of words’’ (p.395).

Wisniewski and Love (1998) state that ‘‘alignment of conceptual structures is

important in understanding conceptual combinations’’ (p.398). Already existing

concepts drives the word-learning process, whereas a new word may drive one to learn

the concept mentioned in a certain context. Name learning can influence or cause

category acquisition, based in part on differences you were already noticing in any case.

Category learning and name learning can go hand in hand, sometimes over an extended

period (Wisniewski & Love, 1998). The specific meanings used with collocational

dependency may cause people to provide different synonyms in different contexts. The

lexical collocations such as fresh water, fresh bread, and fresh air have different

meanings.

Cruse (1986) also suggests that contextual modification is greater for verbs and

adjectives, because their meanings depend on the nouns that they are associated with.

Word meanings cannot fully be represented by typical feature lists used to describe a

single concept. In this sense, the psychology of concepts is little help in explaining the

10

nature of personal interactions, episodic memories and the prior discourse that

influences meaning. Word meanings are strongly associated with interactional and

contextual modules (Cruse, 1986).

Since concepts are nonlinguistic representation of the world, by connecting

words to these representations, we can explain how people can connect sentences and

words to objects and events in the world (Murphy, 2004). Concepts are just the things

that are evoked by our perceptual systems and that control our actions. Thus, by

hooking up words to concepts, we can break out of the circle of words connected to

words and tie language to perception and action. Croft (1991) posits the idea that it is

our basic concepts of objects, events, and properties that lead languages to have similar

morphosnytactic properties.

When analyzing concrete and abstract concepts, entity, situation and

introspective properties are taken into consideration. Murphy (2004) mentions that

entity properties are intrinsic properties that describe the structure and appearance of

objects, and express properties of the items themselves. Introspective or experience–

related properties are personal experiences related to an item. This can include

emotional or evaluative responses, negation, representational states, and more complex

features such as contingencies and causal relations. Some experiential properties are

intrinsic item features, meaning that they describe the item itself. In other cases, they are

relational properties and describe relations of the item to other items or actions.

Situation properties are relational properties, which describe the item’s relations to other

entities in context, such as animate beings, physical and social states, functions, and

locations. Situation properties generated for tree typically include animate beings such

as birds, objects such as soil, actions such as climbing, and functions such as offering

shade. Situation properties reflect knowledge about an item’s context and usually do not

express characteristics of the item itself (Murphy, 2004).

Murphy (2004) says that very concrete concepts contain a large amount of item

properties, and evoke if any experiential properties exist. In contrast, experiential

features are regular part of very abstract concepts, for which few if any concrete item

properties are produced. The more concrete a concept is, the more entity properties and

the fewer experience properties are generated. Abstract concepts involve qualitatively

different types of properties from concrete concepts. Unlike concrete concepts, abstract

concepts have fewer intrinsic and proportionally more relational properties (Barsalou,

2005). Mental processes in particular are triggered by perceptions, but are not

11

themselves perceived. Many abstract concepts seem to require mental processes or

emotions which specify relevant situation aspects and unite them into coherent

concepts. Although abstract and concrete concepts are embedded in knowledge about

typical situations, they may differ in focus. The center of focus for concrete concepts is

the represented object, along with its attributes, parts and functions, actions performed

on or with the object, relations to other objects, and perhaps specific locations. The

focus for abstract concepts encompasses a complex arrangement of entities and

processes. The center of focus likely varies across kinds of abstract concepts. An

abstract concept may involve a person, mental state, relation to some state of affairs,

and/or a state of affairs.

Abstract concepts are relational concepts, which are likely linked to an extensive

number of other concepts (Gentner, 1981). An important part of language

comprehension is the combination of concepts into larger and larger structures guided

by the syntax of language. Smith, Osherson, Rips & Keane (1988) argue that a modifier

appears both to change something about the head concept and to increase the

importance of what has been changed in the resulting concept. The modifier can be a

determining factor in conceptualizing an item. However, Murphy (1988) discusses that

the modifier depends on the noun it is paired with. Gagne and Shoben (1997) analyzed

the interpretation of a number of noun-noun phrases that they systematically

constructed. When they analyzed their own interpretations of these phrases, they

discovered that different nouns tended to be associated with some relations more than

others. The results revealed that the relational preference made a difference for the

modifier but not the head noun. Barsalou (2005) argues that as concepts become

increasingly detached from physical entities and more associated with mental events,

they become increasingly abstract. In abstract concepts one can easily be aware of

structured representations. The process of conceptual combination and metaphorization

is the process of combining individual concepts into structured representations.

Barsalou (2005) mentions some difficulties of tracking abstraction because it is

hard to identify when abstraction is complete and how it is principled. Researchers who

study conceptual combinations, language, and thought all know that structured

representations are not only a sign of human cognition, but are essential for adequate

accounts of these phenomena. There have been a lot of theories and findings related to

concepts and categories, but not to conceptual combinations, as it is subtle to delve into

details of understanding the properties of both concepts. Bringing two concepts together

12

puts a hard problem for both psychologists and linguists. (Medin, Lynch &Solomon

2000).

2.4.2. Firthian and Neo-Firthian Approaches

Firth (1957) defined the term ‘collocation’ as pair words that are closely related

to each other. This approach is seen as empirical since it prioritizes lexical level

providing a possibility for predictability. ‘Mutual expectancy’ was also emphasized in

order to predict the possible collocations in a certain context (Firth, 1957, p.181).

However, this operational and empirical definition of collocations was long ignored

since this definition remained obscure (Evert, 2007). Following the development of

computational linguistics, the definition was revived and applied by Neo-Firthian

researchers (Sinclair, 1991, 2005). Evert (2007) approaches the term empirically by

defining it as recurrent and predictable word combinations. Three approaches regarding

the co-occurrence of words are surface co-occurrence, textual co-occurrence and

syntactic co-occurrence. The first is related to the running text in which words appear

together in tokens. The second approach refers to the analysis of collocations in the

same sentence, clause, paragraph document, while the third approach defines word

combinations in terms of syntactic relations.

In Firthian sense, collocations are recurrent word combinations and frequently

co-occur (Smadja, 1991; Bartsch, 2004). If a word combination occurs more than twice,

it may mean that they have the potential to act together. It is inadequate for word

combinations to be recurrent in a text and is quite important to resort to other

measurements in order to detect weak and strong collocations (Evert, 2004, 2007).

Since recurrence of word combinations may signal an accidental formation in the

sampling, the methodology of statistical association was developed (Sinclair, 2004;

Evert, 2007). Statistical association measures refer to the quantification of the attraction

of word combinations (Evert, 2007). Unlike phraseological explanation that adopts

multiword expressions used synonymously with collocations, Neo-Firthian explanation

views collocations as predictable, recurrent and observable. This approach strives to

search for true collocations with statistical analysis and makes a distinction between

strong and weak collocations according to its statistical association. Empirical approach

of collocations centers statistical probability in order to strengthen the predictability of

collocations. In this sense, statistical association and attractions are common terms used

13

to determine recurrent words (Evert, 2004, 2007). Different association measures of

collocations may generate different results and rankings of collocations. In terms of the

strength of collocativity, two kinds of approaches are broached. The former refers to the

threshold that tries to search for true collocations, whereas the latter denotes a ranking

approach that determines collocations without firm separation. The empirical view of

collocations is also known as node-collocate view that prioritizes the predictability of

word combinations. This approach also attempts to understand how the node (word)

determines its company (collocate). Starting with Firth (1957), this view has gained

considerable acceptance among linguists and corpus –based lexicographers.

Quantification and operational definition of collocations remained premature

and undertheorized in Firth’s studies. However, Sinclair’s studies (1991, 2005)

collocativity was operationalized considerably. Sinclair (1991) altered the course of

language studies by positing the idea of ‘idiom principle’:

The principle of idiom is that a language user has available to him or her large number

of semi-preconstructured phrases that constitute single choices, even though they might appear

to be analysable into segments … (Sinclair, 1991, p.110).

To comment on this passage, it can be remarked that expressions are stored as

structures in the sense of formalist models of grammar but as units that will be retrieved

from the lexicon (Sinclair, 1991). Sinclair (2004) centralizes collocation around the

basis of meaning, adding that ‘‘a grammar is a grammar of meanings and not of words

and the meaning of words together is different from their independent meanings’’

(p.18). Words alone are chosen by users of language. Instead, units of expressions are

conveyed by speakers. Collocation is the cornerstone of his understanding of language.

Lexis and semantics are based on collocations that are completely central in his

linguistic studies (Sinclair, 1991, 1996 & 2004).

2.4.3. Psychological Approaches to Collocations

Two main psychological approaches to collocations have been developed in the

last three decades (Halliday & Hasan, 1976; Leech, 1974; Hoey 2005). The first

approach was developed by Halliday and Hasan (1976) that regard the collocation as a

cohesion device associated with each other as a result of co-occurrence in similar genres

or texts. According to this view, similar texts produce similar and strong collocations.

Therefore, collocations are, in Halliday and Hasan’s model, are termed as textual

14

collocations. In this sense, it can be said that textual collocations entail textual cohesion

and vice versa. They do not stress the significance of regular co-occurrences that are

closely related. The association in word combinations they refer to is psychological and

takes place in the mental lexicon. Collocations are viewed as a part of coherence and

cohesion system that places semantic relations at the heart of the system. Halliday and

Hasan (1976) approach collocations in terms of their frequent uses and co-occurrences

and assess them paradigmatically. The same word may be used with different

collocates, which implies that the text connects collocations internally according to its

cohesive relations. Thus, lexical cohesion relations between words are pivotal for the

model of Halliday and Hasan. The cohesive force and power of the text is centralized in

the model since the collocates the word takes vary according to the text.

The second psychological view is supported by Leech (1974), who denotes that

‘collocative meaning’ that refers to ‘‘the associations a word acquires on account of the

meaning of words which tend to occur in this environment’’ (p.20). This definition is a

combination of both psychological and statistical realities in that it strives to account for

a deterministic relation between the two. Leech’s approach towards collocations is

causal. Since the two approaches view collocations as secondary in their models and do

not centralize word combinations, Hoey (2005) develops another psychological model

labeled lexical priming that centralizes collocations in linguistic theory.

2.4.4. Lexical Priming Approach to Collocations

Hoey (2005) argues that either statistical nor pure psychological approaches

seem to be adequate to account for collocations thoroughly. Therefore, a further step

needs to be made so as to theorize collocations in linguistics. Unlike the aforementioned

approaches, Hoey (2005) has attempted to centralize collocations at the heart of

linguistic studies. Although other approaches, even the ones not mentioned in this

study, regard collocations as a part or a subpart of language studies, Hoey’s

understanding of collocations is different from that of others in that collocations in his

model are given an utmost role in linguistic studies.

Hoey (2005) defines collocations as ‘‘a psychological association between

words up to four words apart and is evidenced by their occurrence in corpora’’(p.5).

Hoey (2005) emphasizes the fact that a sentence may be grammatical, lexically

acceptable, textually appropriate and meaningful. However, they differ in terms of one

15

being usual and normal collocations and the other being unusual collocations. A

sentence that is regarded to consist of collocations can be evaluated in terms of its being

natural or clumsy. According to Hoey (2005), collocations are pervasive in language;

therefore they need to be analyzed psychologically. Thus, collocation is basically a

‘psychological’ notion for him. Priming in its psychological sense can be the

appropriate term or tool in order to account for ‘pervasiveness’ of collocations in a text

(Hoey, 2005, p.7). Even use of metaphors can be the same in a text. However, what

would sound natural and clumsy in a text results from the use of collocations but not

from that of grammar or words alone. Hoey (2005) supports the idea that ‘‘every word

is mentally primed for collocational use’’ (p.8). Texts and co-texts where collocation

occurs stand for ‘nesting’ for this approach. Stubb’s (1996) idea of language is also

supporting this view in that it is noted that ‘‘speakers are free, but only within

constraints’’ (p.56). Hoey (2005) adopts this conception because lexical priming of

collocations is closely associated with constraints that refer to systematicity in lexis. In

traditional linguistics, grammar is understood as systematic, and lexis is defined as

loosely organized. However, Hoey (2005) insistently stresses that the situation is just

vice versa. Lexis is systematic since it can be used only within constraints, and grammar

is loosely organized. In this sense, it can be remarked that ‘‘collocations are limited to

particular domains and genres’’ (Hoey, 2005, p. 9). This psychological model posits the

idea that just as computers concordances exist, our minds also function as concordances

as our minds are culturally, discursively, linguistically, physically enriched and

immersed. In this sense, it can be clearly concluded that from Hoey’s perspective every

word is primed to occur with particular other words and particular semantic sets in

particular discourses or co-texts. Since Hoey (2005) focuses on particular domains, he

puts forward the idea that corpora should be specialized but not general because general

corpora do not give a full account of collocations since even the same word has

different collocates in different texts.

Although endorsing some aspects of some models of collocations, Hoey (2005)

criticizes Neo-Firthian approach in particular by stating that ‘‘it gives no clues as to

why collocation should exist in the first place’’ (p.4). The approaches mentioned above

stress only the importance of word combinations or co-occurrences and ignore some

important characteristics of collocations. A combination of collocations and

constructions has also become prevalent in linguistic studies. Collocations are only

16

word combinations but also extensions of constructions or constructions themselves in

modern linguistic theories (Diessel, 2004; Goldberg, 2006; Gries, 2012).

In line with the development in formulaic language studies, in Turkish context,

the studies regarding lexical collocations have also been prolific only recently

(Alpaslan, 1993; Eker, 2001; Ördem; 2005; Çetinkaya, 2009; Balcı and Çakır, 2012;

Bıçkı, 2012; Durrant, 2013). These studies largely focused on written production of

lexical collocations in foreign language learning and language studies such as dictionary

preparation or typological studies in Turkey. Although some experimental studies of

lexical collocations have also been carried out in Turkish context in the last few decades

(Altınok, 2000; Gencer, 2004; Ördem, 2005; Balcı & Çakır, 2012), these studies have

contributed to gaining awareness of collocations. Balcı and Çakır (2012), different from

the previous studies in Turkey, focused on the primary language learning and found that

collocation training was important to help young learners learn a foreign language

better. However, these studies in Turkish context have not incorporated both lexical and

linguistic studies concurrently. Therefore, it is important that constructions of lexical

collocations should be included into the studies because construction grammar and

lexical collocations are closely related to each other.

2.5. Construction Grammar

Construction grammar deals with all levels of language taking each specific

construction seriously without refusing any regular or irregular construction (Goldberg,

2006; Fillmore, 1979; Kay & Fillmore, 1999; Croft, 2004; Langacker, 1991). Not only

sentences but also words have constructions and grammatical properties. Pure sentential

grammar does not exist. Construction grammar refuses the sharp distinction of grammar

and lexicon. Rather, grammar is formed through constructions made up from lexis. In

this sense, the idea of formulaic language is prevalent in construction grammar.

Construction grammar does not handle only words but also phrases as well as any

central or peripheral construction. However, since this study specifically focuses on

lexical collocations, the lexical aspect of construction grammar will be analyzed.

Golberg (2006) posits the idea that each word may have its own intrinsic features.

Therefore, it is worth considering the specific properties of each word class. Each

construction in language refers to specific constructions. In parallel with this idea,

collocations may denote specific constructions as well. Therefore, collocations are also

17

included in this theory (Goldberg, 2006). Construction grammar in line with the purpose

of this study attends to the item-based knowledge and exemplar models of

categorization by presenting data of verbs. Speakers of a language reach

generalizations through exemplars that enable speakers to generalize lexical patterns.

Our knowledge of instances allows us to make generalizations which can be reached

through statistical properties of the features of the stored exemplars in mind. Goldberg

(2006) in her study concludes that ‘‘verb-centered categories are categorized together,

ultimately resulting in general, abstract argument structure constructions’’ (p.59).

Arguments of verbs refer to collocations in a sense. Humans may not retain or store an

infinite number of utterances. In contrast, speakers reach generalizations over similar

exemplars. This idea of construction grammar is similar to Stubbs’ idea (1996) that

‘speakers are free but only within constraints’ (p.56), which is also endorsed by Hoey

(2005). Construction grammar regards speakers as conservative and attentive in that

speakers do not use language beyond what is given or used and become consistent users

of the input they encounter. In this sense, each word is conventionally related to rich

frame semantic meaning that determines its arguments. This idea resembles Hoey’s

model in that texts and co-texts determine collocates a word may take. The given frames

breed appropriate participant and argument roles. This principle is called semantic

coherence principle. Halliday and Hasan’s textual cohesion and Hoey’s co-textual

understanding of collocations can be likened to this model in construction grammar. As

such, specific and particular frames or texts generate specific and particular arguments,

participants and collocates. In parallel with construction grammar, Gries and

Stefanowitsch (2004) produce a new term collostruction, a mixture of construction and

collocation. This term is quite useful in terms of establishing the basis of collocations in

a linguistic theory. Based on the idea of construction grammar, collostructions do not

show a sharp distinction between grammar and lexis. Rather, both grammar and lexis

are regarded as having a continuum rather than a dichotomy. An adequate frequency of

a construction may be a predictor of a specific usage in language. Even these

constructions can be predicted through the help of the frequency, they still remain as

constructions (Goldberg, 2006).

18

2.5.1. Collostructions Analysis

Since lexis and grammar have been long considered totally distinct from each

other, the emergence of new theories such as pattern grammar (Hunston & Francis,

2000) and lexical chunks (Lewis, 1998; Sinclair, 1991) has changed the attitude towards

these two distinct categories in linguistics. Grammar and lexis are seen as a continuum

rather than as a dichotomy. These approaches emphasize the importance of

constructions and lexis together, and collocations in particular are given a pivotal role.

A reasonable mixture of these lexical and grammar approaches has also emerged. As a

result of this line of thinking, a new term, collostruction, has come into use (Gries &

Stefanowitsch, 2004). Collostruction is a mixture of collocation and construction. In

other words, collocation use originates from lexical approaches, and construction comes

from the theory of construction grammar and pro-construction grammar theories. The

term collostruction used to quantify collocational strength between words with their

grammatical properties is a blend of collocational and constructional approaches. In this

sense, collostructional approach produces more reliable results than frequency data.

Collostructional analysis identifies the association strength between a certain

construction and lexical items (Gries & Stefanowitsch, 2004). Collexeme refers to any

single word that is analyzed according to its constructions. Lexemes are always

adjoined to a particular construction. If a construction is related to a particular lexeme, it

is called collostrcution. If there is a combination of collexeme and collostruct, it is

labeled as collostruction. The extension of words and collocations is of paramount

importance to collostruction analysis because traditional collocation studies disregard

collostructions and deal with only raw frequency and words. Therefore, collostruction

analysis has arisen out of this need in order to fill this gap methodologically. The main

contribution of collostruction analysis is that it strives to take multidimensional analysis

of frequency of the data into consideration. By applying inferential statistics,

collostruction analysis acknowledges the importance of frequency statistically.

2.6. Corpus Linguistics

Corpus linguistics has developed considerably over the past two decades. Corpus

linguistics is an approach that permits language researchers not to be dependent only on

intuition. A corpus is a machine-readable compilation of texts stored in order to

19

represent some part of a language and conduct empirical analysis making use of ready-

made data. Corpus is prepared with ‘particular purposes in mind’ and represents only

some part of language (McEnery, Xiao & Tono, 2006, p.4). Texts are processed through

computers and software programs that allow data to be identified and analyzed in

qualitative and quantitative analytical techniques. A distinction should be made between

corpus based and corpus-driven language study (Tognini-Bonelli, 2001). The former

refers to the studies that use corpus data in order to prove, disprove or discover

hypotheses or theories, while the latter takes corpus data as a methodological basis and

supports the idea that corpus is not a secondary tool to carry out research but a primary

tool to develop hypotheses about language. This understanding of corpus linguistics is

closely related to Neo-Firthian approach that takes language studies as empirical

through software programs (Evert, 2007; McEnery & Hardie, 2012). The second

approach puts forward the idea that corpus-driven research has not only provided a

robust methodological and quantitative tool but also led to a theoretical and qualitative

innovation because a number of theories have had to revise the assumptions they

posited before (Tognini-Bonelli, 2010).

There are mainly four modes of corpora that consist of spoken, written, video

record paralinguistic features in order to research mimes and gestures and sign

language. Written corpora are the most commonly used and most developed corpora

modes because it is labor-intensive to convert spoken data into transcripts. The corpora

of video records and sign language are quite recent additions to the field (McEnery,

2012). A great number of corpora contain more than one mode and are composed of

written and spoken modes. A researcher can carry out comparative research between

modes through corpora. Another issue related to corpora is data collection procedure

that poses some problems methodologically. However, there are two mainstream

approaches to collect data. The first one is monitor corpus approach that deals with the

constant expansion and development of corpora in size over time and addition of

various modes and materials. The second approach is balanced corpus or sample corpus

that is constituted for a specific purpose in accordance with a specific sampling.

Balanced corpora research entails a valid and reliable representativeness and population

within a particular sampling frame.

Methodology in corpora studies have been exposed to criticisms (Leech, 2007;

Varadi, 2001; Biber, 1993). It is hard to compose an encompassing corpus that

represents the data empirically. Leech (2007) positively and optimistically states that

20

corpus design problems should not discourage researchers from determining their own

representativeness inasmuch as it will take a long time to fully define and determine

sampling, population and representativeness. The better idea is that a researcher should

aim at achievable designs rather than abandon this aim.

Annotation is another term in corpus linguistics that deals with encoding data in

the text. Some linguistic analyses are performed encoded or unencoded. Encoding

stands for annotation in corpus linguistics. It may be necessary for some researchers to

edit the text directly or add only mnemonic code to the words the researcher has

determined. For example, the word ‘remark’ can be a verb or noun in a sentence. If one

wishes to specify as noun, then s/he can edit it directly and codes it as N. Instead of

editing it as N, the word remark can be underscored. Annotation is not a must-have tool

to be used. Even some corpus linguists oppose the use of corpus annotation since the

analyses may be inaccurate or inconsistent. Thus, a corpus text can be undertaken

manually or automatically, in other words annotated or unannotated (Sinclair, 2004,

2005).

Another important aspect in corpus methodology is full accountability that

entails an arrangement of a specific theory by the researcher so that data can be obtained

easily from the prepared corpus (Leech, 1992). However, this conscious choice of data

can give rise to bias in a study. Random choice of the corpus texts provides

accountability. Since even random choice of corpus data is only a small sampling of the

whole language, it always remains exposed to criticism in terms of not being able to

provide full accountability. However, the dataset formed can satisfy accountability

partially or wholly. Therefore, falsifiability and replication together provide the validity

and reliability of corpus studies since corpus linguistics is an empirical and scientific

endeavor rather than merely data gathering tool. The main issue that linguists have to

think about is not accessing a large amount of data but rather choosing a dependable

methodology in order to assess the data reliably (Tognini-Bonelli, 2010). Therefore, the

choice of appropriate methodology gains importance.

Corpora enable researchers to employ mainly descriptive statistics and

significance tests in accordance with the design chosen. Since the amount of data

gathered is immense, quantitative data can be tested and processed through statistics.

Descriptive statistics in corpora research do not test significance levels. Rather, it

basically gives frequency counts or percentages and type-token ratio. If one wishes to

go beyond descriptive statistics, then significance tests can be conducted. According to

21

the type of data, chi-square test, t-test, log-likelihood can be performed. Factor and

cluster analysis can also be done in order to determine whether the chosen variables are

related to each other.

2.7. Written Academic Disciplines

Academic writing involves a social, interpersonal and interactional process that

can be defined as labor-intensive and complex in terms of its cognitive and linguistic

content (Hyland, 2012). Academic writing is a conscious effort to conform to the rules

of certain genres from different disciplines. Hyland (2000) and Swales (2004)

emphasize the fact that a genre presupposes that certain conventions will be followed by

writers and even readers that expect writers to convey information in accordance with

these conventions. Therefore, written genres have idiosyncratic rules and conventions

determined by both writers and readers. In this sense, writing is a socially

conventionalized form of language that accommodates constraints. Each academic

genre has its own historical background, social and linguistic constraints. It is important

to understand why certain genres produce certain language and how they use certain

forms or words. Each genre represents a particular social world and reflects particular

linguistic features. Genres are classified in terms of their typology. Therefore, academic

disciplines have categorical distinctions in order to specify certain genres.

Disciplines are divided into hard or soft and pure or applied (Becher & Trowler,

2001). Natural sciences and mathematics are defined as hard-pure. Engineering is

accepted as hard and applied. Humanities such as philosophy are classified as soft-pure.

Social professions such as education and law are described soft and applied. In terms of

linguistic features, differences are markedly observed between these disciplines. Hyland

(2000) state that soft disciplines use more varied verbs than hard sciences since hard

sciences are based on observation and quantitative data and search for causal and

determinantal links, whereas soft sciences tend to use more discourse acts and endorse

their idea by using references to other writers. However, hard sciences are prone to

convey knowledge directly based on the immediate method or research tool. Pure

sciences often refer to theory and research know-that rather than know-how. Know-how

requires research in the field and present practical knowledge. This typology of different

genres brings about disciplinary differences in language use in academic writing.

22

Recent studies regarding these disciplinary areas have focused on lexical verbs

since use of varied verbs may be used as evidence to show the variations across the

disciplines. Hinkel (2004) mentions five different kinds of verbs across the disciplines.

1. Activity verbs (make, use, put, give)

2. Reporting verbs (suggest, propose, argue)

3. Mental/Emotive verbs (know, think, see)

4. Linking verbs (appear, seem, remain, keep, prove)

5. Logicosemantic relationship verbs (contrast, cause, follow)

Lexical verbs denote writers’ stance, rhetorical use of language, position and

attitude towards the topics. Reporting verbs in particular has received attention in

corpora studies of academic texts (Hyland, 2000, 2012; Charles, Pecorari & Hunston,

2009; Hunston, 2009). Reporting verbs help writers position themselves, show their

attitudes and accept or refuse the claim made.

The next chapter will focus on the methodology of the study in detail by

providing the nature of the data and the tools used in this study.

23

CHAPTER 3

METHODOLOGY

3.0. Introduction

The present chapter deals with the design of the study. The rationale and

procedure behind the study will be explained. Firstly, some brief information related to

the corpora will be presented. Corpus selection method, corpus size, quantitative

measurement tools are given in this section. Secondly, the issue of frequency and

statistical tools will be made clear. Following this, the analysis of the results from the

perspective of construction grammar will be handled.

3.1. Research Design

Since this study aims to describe and analyze lexically-initiated verb+noun

lexical collocations across written academic genres, and intends to discover whether

there are prototypical verb+noun collocations and collostructions across and within

three different sciences, health, physical and social sciences and to examine these

collocations in terms of construction grammar, thus the scope necessitates establishing a

design considering corpus-driven approach together with theoretical analysis. Hunston

and Francis (2000) refer to this kind of methodological design as below:

Potentially, then, we have two competing (or complementary) sets of generalizations

arising from a corpus, one that depends on entirely on frequency of co-occurrence and is

able to be generated by computer software alone, and one that is more interpretative and

demands the input of a human researcher. How these sets of generalizations might differ

from each other, and the implications of such difference are topics that have yet to be

explored (Francis, 2000, p.27).

This definition of the methodology in language studies can be regarded as a

referential point of this study. Therefore, the bidirectional perspective of the

methodology enables the study to look into lexical collocations in a richer context

24

(Biber, 1998; Fillmore, 1992; Leech, 1992). Table 1 shows the steps followed in

conducting the study.

Table 1

Research Type and Stages of the Study

Stages Process Research type Stage 1 Selection of articles from journals Corpus-based approach Stage 2 Formation of corpora from 249 research

articles from 44 journals of health, physical and social sciences.

Corpus-based approach

Stage 3 Conversion of corpus into text format Corpus-based approach Stage 4 Automatic generation of frequency lists Descriptive analysis Stage 5 Selection of meaningful lexical

collocations manually Corpus-based approach

Stage 6 Application of statistical analyses across and within corpora (Fisher`s exact test)

Quantitative corpus analysis

Stage 7 Checking prototypical lexical collocations Descriptive and Interpretative Stage 8 Analysis of prototypical lexical

collocations through construction grammar

Interpretative

3.2. Data gathering procedure

The database of this study was formed from 249 research articles from 44

journals of health, physical and social sciences.

3.2.1. Written Academic Corpora

The corpora for this study were retrieved from internationally recognized,

electronic journals research articles (RA). A corpus of 249 research articles (116 for

health: 84 for physical, and 49 for social sciences) included 1,217.197 words. Each

science type was planned to have the similar number of words. Therefore, the number

of articles varied but the number of the words for each science remained similar (see

Table 2). Recent articles published between 2009 and 2011 were chosen.

Only professional texts were chosen from the journals of three mainstream

sciences to gain an insight into the analysis of across and within disciplines.

25

Table 2

The Overall Data of the Texts

Science type Number of

disciplines

Number of

research articles

Years Total words

Health science 20 116 2009-2011 405,753

Physical science 14 84 2009-2011 405,751

Social science 10 49 2009-2011 405,693

Totals 44 249 2009-2011 1,217.197

Table 2 indicates that the number of the words in each genre was rendered

almost equal so that more reliable results could be obtained between and across the

genres. The number of the disciplines varied because each discipline has a different

number of pages and words. However, the number of the words remained similar. The

disciplines for each genre are shown in Table 3.

Table 3

Disciplines Chosen for the Corpora in Three Distinct Sciences Health science Physical science Social science Anatomy Anesthesiology Bacteriology Brain-Neuroscience Cardiology Cell Biology Dentistry Dermatology Endocrinology Gastroenterology Genetics Geriatrics Immunology Internal medicine Nephrology Ophthalmology Pediatric Physiology Psychiatry Radiology

Agriculture-Plant sciences Astronomy Bioengineering Botany Chemistry Chemical and Materials engineering Civil Engineering Environmental Sciences Geology Marine Science Mechanical Engineering Meteorology and Climatology Physical Geography Physics

Literature Anthropology Education Gay and lesbian studies Law Philosophy Political Science Psychology Recreation and Sports Sociology

26

These texts were transformed into text format in order to create an electronic

corpus of 1,217.197 words. Lexical collocations, specifically verbs, were extracted from

the corpus. Since the aim of this study was to analyze verb+noun lexical collocations,

other word classes were excluded. The classification of the collocations was done in

accordance with the operational definition.

3.3. Empirical Methods in Corpus Linguistics

Corpus linguistics is an empirical discipline that encompasses both qualitative

and quantitative analysis. The qualitative aspect of corpus linguistics refers to a basic

description and analysis of real texts obtained from a corpus. However, the quantitative

property of corpus linguistics is based on the idea that corpus can be analyzed and

evaluated statistically. It should be borne in mind that corpus linguistics is not a main

sub-discipline of linguistics but rather is regarded as ‘a group of methodologies for

linguistic analysis’ (Leech, 1992, p.79). It provides a very efficient tool in order to

analyze real texts empirically. Therefore, it can be said that almost any corpus-driven or

corpus-based approach is a quantitative empirical study that entails simple or complex

statistical analysis ranging from frequency to highly sophisticated statistical tools.

Scientific quantitative research and data enable researchers to examine and explain the

results more carefully. It is important to reach objective results in language studies.

However, the use of corpus does not rule out the use of intuition (Holtz, 2011). Fillmore

(1992) supports the idea that corpus and intuition are complementary rather than

dichotomic.

This study uses both descriptive and analytical statistics. The former statistics

describes and classifies data by providing a visual picture of the study, while the latter

tests the significance level of the collected data.

3.3.1. Descriptive Statistics

Descriptive studies show frequencies of data quantitatively in histograms,

graphs, pie or bar charts or scatter plots (McEnery & Hardie, 2012). Such quantitative

data generally shows superficial features of the collected data. Therefore, frequency lists

provide a general picture of the corpora. A detailed analysis of significance level of the

27

data obtained from the corpora entails analytical statistics so that researchers can assess

and interpret data thoroughly, which requires inferential statistics.

3.3.2. Analytical Statistics

Analytical statistics is used to interpret the data in terms of significance level by

taking hypotheses into consideration. A quantitative corpus linguistic tool involves the

use of one of the statistical tools (McEnery & Hardie, 2012)

Since this study chose the texts randomly from the database, the data are not

normally distributed, and the variances are heterogeneous. Chi Square, a non-

parametric, was chosen in order to see whether differences between the samples are

statistically significant or not. Chi Square can be used for non-normally distributed data.

3.4. Data processing and Analysis Procedures

A great number of basic and sophisticated quantitative tools have been

developed in recent years (Evert, 2007; Holtz, 2011). This study is made use of both

descriptive and analytical statistics. The texts were not tagged and not annotated. The

verbs were extracted manually. Subsequent to this process, verb + noun collocations

were chosen manually from wordsmith and concordancer program because the software

programs cannot identify verb+ noun collocations. The analysis of the study was

twofold.

3.5. Software Programs

Since each type of software has distinct properties, two different types of

software were used in order to reach reliable results. Some software programs are

available on internet and are easily accessible and easy to use at a basic level. The

first software used in this study was concordance that provided the basic results (Watt,

2012). This software does not carry out detailed inferential statistics but offers basic

descriptive statistics. Counting words, making word lists and word frequency lists, full

concordances, choosing pick lists, using multiple input files are among the functions of

this software. The second software utilized was Antconc that offers a better service

28

because Antconc provides multi-layered results composed of clusters, concordance plot

and basic statistical measurement. The basic statistical tools in Antconc are log-

likelihood, average value and clustering. Although it does not present a detailed

statistical measurement, it was used for the basic statistical results. The third software

was Wordsmith, a relatively sophisticated and integrated corpus software program used

for text processing and extracting verb+noun lexical collocations descriptively and

inferentially (Scott, 2010). A few steps should be followed in order to reach expected

results. In this sense, Wordsmith offers to generate word lists according to its

alphabetical and frequency order, concordance, to find collocations and show

frequencies altogether with statistical tools. It can also compare different texts by

showing their statistical significance level. Wordsmith is highly developed software

compared to the first two. It basically contains three modules composed of Concord,

Keyword and Wordlist. T-score, chi-square score, Z-score and mutual information

analysis can be performed through the Wordsmith software program. This software

offers several important services such as generating concordances, listing occurrences

and co-occurrences of the key words in a given text, comparing words and carrying out

basic statistical analysis.

3.6. Coding of verb+noun Collocations

Lexical collocations may refer to a broad range of variations. Complexity in

classifying verbs and nouns renders proper coding necessary. Therefore, it is important

to limit the variations in a study. In this study, only verb+noun collocations were

handled. However, even verbs and nouns alone denote intricate variations. Although a

wide range of criteria could be used owing to the complexity of verbs and nouns, only

certain criteria were determined in this study:

a. Verb+noun collocations do not necessarily have to be transitive verbs. Not all

verbs that take nouns have to be transitive.

Ex 1 : It seems a problem.

b. Verbs that are used in passive constructions and take infinitive forms were

ignored.

Ex 2: Reflection has been found to compromise.

29

c. Verbs that have to take some prepositions when they co-occur with a noun

were added.

Ex 3: Women can deal with stress more easily.

d. Verbs that are combined with a particle and form phrasal verbs were ignored.

Ex 4: We found out that men resort to violence more .

e. Nouns that are followed by infinitive and gerund were removed.

Ex 5: We intended to focus on different criteria.

f. Nouns that are followed by noun clause were classified as verb+noun

collocations since noun clauses are extensions of nouns, and it is important to

determine noun clauses in construction grammar. Thus, noun constructions

were added to verb+noun collocations.

Ex 6: Previous research has found that people tend to consume fast food .

g. Verbs that are transitive but have not taken any kind of collocations or

collostructs were ignored.

Ex 7: People know.

The next chapter will focus on the results and discussion of the findings by

providing selected examples from the concordancer.

30

CHAPTER 4

RESULTS AND DISCUSSION

4.0. Introduction

This chapter deals with the results of the verb+noun collocations using

descriptive statistics through software programs. Firstly, the descriptive results of the

key words used in the corpus are given. Secondly, the collocations and the main

features of these collocations are presented. A comparison between the disciplines is

given in order to find prototypes of the collocations across the texts. Thirdly, the

collostructions of these collocations are analyzed.

4.1. Overall Results of the Study

The overall descriptive results of the key words used in the texts were given, and

a summary statistics of the texts themselves was presented. Figures (4.1.), (4.2.) and

(4.3.) present the summary statistics of the three disciplines.

Figure 4.1. Summary statistics of the health science texts

31

Figure 4.2. Summary statistics of the physical science texts

Figure 4.3. Summary statistics of the social sciences texts

As indicated in Figures (4.1.), (4.2.) and (4.3.), the number and ratio of types and

tokens across the three genres were intended to be balanced so that the sampling could

be reliable. The ratios of types and tokens were 5.77% in health sciences, 5.80% in

physical sciences and 6.59% in social sciences. The relatively slight ratio difference in

32

social science stems from the nature of the interpretative aspect of social sciences. After

the summary statistics of the words given, the frequency of the verbs that co-occurred

with nouns was given in Table 4.

Table 4

Descriptive Statistics of Verbs According to the Tokens

Academic genre Total words Verbs with collocates %

Health science 405,753 8740 2.15

Physical science 405,751 7298 1.79

Social science 405,693 12206 3.00

Total 1,217.197 28244 2.32

The percentage of the verbs in Table 4 shows a similar variation. The percentage

of the verbs in social science is the highest (3.00%), while physical science forms the

lowest percentage (1.79%). Health science accounts for only 2.15% of the verbs. Table

5 exhibits the ratio of verbs considering the types.

Table 5

The Overall Statistical Results of Verbs According to the Types

Academic genre Total words Collocational verbs %

Health science 23.408 724 3.09

Physical science 26.717 556 2.08

Social science 23.522 920 3.91

Total 73.647 2190 2.98

It can clearly be seen from Table 5 that the collocational verbs in the social

sciences account for the highest percentage (3.91%), whereas the verbs in the physical

sciences constitute the lowest percentage (2.08%). The percentage of the verbs in the

health sciences is only 3.09%. The total percentage of the verbs in terms of types is

2.98%.

33

4.2. Results Related to Research Question (1)

Research question (1): What verb+noun lexical collocations can be observed

across academic genres?

Each academic genre produced different results in terms of verb+noun

collocational variations. A close look at the first 100 verbs used with meaningful

collocations extracted from the concordancer shows the variations in the frequency and

percentage of the verbs encountered in the health sciences.

4.2.1. Verbs and Collocates in the Health Sciences

Although the number of the verbs found in the health sciences was composed of

714 verbs with collocational potential, the most frequent hundred verbs were given. The

frequency and percentage of these verbs are given in Table 6

Table 6

The Frequency and Percentages of Verbs in the Health Sciences

N Verbs Frequency Percentage

1. achieve 15 0,36

2. activate 29 0,70

3. adopt 17 0,41

4. affect 58 1,40

5. allow 34 0,82

6. alter 13 0,31

7. analyze 12 0,29

8. assess 68 1,64

9. assume 12 0,29

10. avoid 25 0,60

11. become 18 0,43

12. believe 21 0,50

13. capture 10 0,24

14. cause 55 1,33

15. clarify 13 0,31

16. compare 46 1,11

34

17. conclude 13 0,31

18. confirm 29 0,70

19. consider 26 0,62

20. contain 29 0,70

21. continue 21 0,50

22. continue 21 0,50

23. contribute 59 1,42

24. define 17 0,41

25. describe 25 0,60

26. detect 70 1,69

27. determine 110 2,66

28. develop 31 0,75

29. discuss 9 0,21

30. display 26 0,62

31. enhance 25 0,60

32. ensure 27 0,65

33. establish 16 0,38

34. evaluate 52 1,25

35. evoke 13 0,31

36. examine 43 1,04

37. exclude 24 0,58

38. exert 17 0,41

39. exhibit 25 0,60

40. explain 39 0,94

41. explore 22 0,53

42. express 23 0,55

43. facilitate 22 0,53

44. find 311 7,52

45. follow 24 0,58

46. fulfill 9 0,21

47. generate 24 0,58

48. get 15 0,36

49. give 19 0,45

50. identify 41 0,99

51. illustrate 10 0,24

35

52. impair 9 0,21

53. implement 12 0,29

54. imply 9 0,21

55. improve 54 1,30

56. include 92 2,22

57. indicate 118 2,85 58. induce 47 1,13

59. inhibit 56 1,35

60. interface 33 0,79

61. involve 18 0,43

62. know 13 0,31

63. maintain 28 0,67

64. make 33 0,79

65. manage 22 0,53

66. mediate 10 0,24

67. modify 10 0,24

68. modulate 15 0,36

69. observe 14 0,33

70. obtain 31 0,75

71. offer 11 0,26

72. perform 25 0,60

73. possess 12 0,29

74. predict 22 0,53

75. prevent 46 1,11

76. produce 38 0,91

77. promote 25 0,60

78. provide 102 2,46

79. raise 9 0,21

80. reach 9 0,21

81. receive 32 0,77

82. recognize 16 0,38

83. recommend 11 0,26

84. reduce 56 1,35

85. reflect 33 0,79

86. remove 15 0,36

36

87. represent 67 1,62

88. require 36 0,87

89. retain 9 0,21

90. reveal 15 0,36

91. select 14 0,33

92. share 11 0,26

93. show 133 3,21

94. speculate 11 0,26

95. stimulate 9 0,21

96. suggest 125 3,02

97. take 23 0,55

98. treat 11 0,26

99. undergo 20 0,48

100. understand 21 0,50

The descriptive results in Table 6 show that the verb find forms the highest

percentage (7.52%). The percentage of most frequently used ten verbs accounts for

28.89% of the total verbs on the list. The second most frequently used ten words

compose 12.75% of the verbs extracted from the corpus, which means that the first most

frequently twenty verbs account for 41.64% of the total verbs. The percentages of the

most frequently used twenty verbs range from 7.52% to 1.11%. The data in Table 6

shows that the scientific articles in the health science used similar words while

composing an article. The first most frequently used ten verbs account for the highest

percentage out of 714 verbs (28.89%). Specifically, the percentage of the seven verbs

(find, show, suggest, indicate,determine, provide and include) is over 2%, whereas the

rest of the verbs extracted are below 2%. In terms of the frequency of collocations, the

most frequently used ten verbs co-occurred with over 60 collocates. The second most

frequently used ten verbs are composed of over 40 collocates. The first most frequently

used verbs are used with over 100 collocates. In terms of the frequency and percentage,

the most frequently used six verbs (with frequency of above 2%) are find, show,

suggest, indicate, determine, provide. Therefore, it is important to show the collocates

of the most frequently used six verbs inasmuch as they form the highest percentage of

the total verbs. The collocations of the most frequently verb find are indicated in Table

7.

37

Table 7

Frequency and Percentages of find Collostructs and Collocates

N Collocates of find Frequency % 1. that –clause 92 29,58 2. difference 17 5,46 3. correlation 7 2,25 4. association 6 1,92 5. evidence 5 1,60 6. decline 4 1,28 7. effect 4 1,28 8. changes 3 0,96 9. correlation 3 0,96 10. application 2 0,64 11. expression 2 0,64 12. trend 2 0,64 13. relation 1 0,32 14. distance 1 0,32 15. decrease 1 0,32 16. result 1 0,32 17. prevalence 1 0,32 18. action 1 0,32 19. sensitivity 1 0,32 20. degree 1 0,32 21. identification 1 0,32 22. symptom 1 0,32 23. findings 1 0,32 24. dissociation 1 0,32 25. diversity 1 0,32 26. increase 1 0,32 27. incidence 1 0,32 28. activity 1 0,32 29. pattern 1 0,32 30. support 1 0,32 31. level 1 0,32 32. sign 1 0,32 33. cause 1 0,32 34. problem 1 0,32 35. automaticity 1 0,32 36. disequilibrium 1 0,32 37. impairment 1 0,32 38. study 1 0,32 39. peak 1 0,32

Table 7 shows that that-clause composes 29.58% of the collocates, and is the

most frequently used collostruct of the verb find. However, the word, that is not used as

a lexical content word. Rather, it is used as a collostructional unit. In its literal meaning

of lexical collocates, the collocate difference was used 17 times. The first thirteen

collocates are used more than at least twice. Since there are often experimental studies

38

in the health sciences, it is reasonable that the collocate difference is the most frequently

used lexical collocate (n=17). Another two frequently used words are correlation and

association collocates that were used 7 and 6 times respectively. The collocate evidence

was used 5 times. The collocates decline and effect were used 4 times in the context.

The collocates effect and change were used 3 times: application, expression and trend

were used 2 times only. The rest of the collocates were used only once in the corpus.

The percentage of the most important collocates and collostructs of the verb find is

shown in Figure 4.4.

Figure 4.4. Frequency of find collocates

The collocates in Figure 4.4 show that the scientific words as collocates co-occur

more frequently with the verb find. The collostruct that-clause as noun complement

clauses accounts for the highest frequency, while the collocates difference, correlation,

association and evidence account for the highest frequency compared to the other

collocates. The important collocates of the verb find were extracted from the

concordancer and shown in Figure 4.5.

39

Figure 4.5. Concordance lines of find collocates

Continued Figure 4.5.

40

Continued Figure 4.5.

The verb show was ranked as the second highest word combined with collocates

(n=133). The frequency of the collocates of the verb show is given in Table 8.

41

Table 8

Frequency and Percentages of show Collostructs and Collocates

N Collocates Frequency %

1. that-clause 44 33,08

2. difference 7 5,26

3. correlation 4 3,00

4. increase 3 2,25

5. relationship 3 2,25

6. abnormality 3 2,25

7. collection 2 1,50

8. reduction 2 1,50

9. change 2 1,50

10. importance 2 1,50

11. activation 2 1,50

12. affinity 1 0,75

13. association 1 0,75

14. complexities 1 0,75

15. dependence 1 0,75

16. dependence 1 0,75

17. feature 1 0,75

18. gaze 1 0,75

19. injury 1 0,75

20. level 1 0,75

21. number 1 0,75

22. organization 1 0,75

23. pattern 1 0,75

24. production 1 0,75

25. rate 1 0,75

26. reaction 1 0,75

27. similarity 1 0,75

28. trend 1 0,75

29. validitiy 1 0,75

42

Figure 4.6. Frequency of show collocates

It can be observed from Figure 4.6 that the collostruct that as a noun

complement clause accounts for the highest frequency, while the collocate difference

forms the second highest frequency. The collocates correlation, increase, relationship

and abnormality are among the most frequently occurring collocates compared to the

rest. The most frequently used collocate of the verbs find and show is difference.

Figure 4.7. Concordance lines of show collocates

43

Continued Figure 4.7

Continued Figure 4.7

44

Continued Figure 4.7

The verb suggest was ranked as the third most frequently used word that co-

occurred with 133 collocates. The collocates of the verb suggest are shown in Table 9.

Table 9

Frequency and Percentages of suggest Collostructs and Collocates

N Collocates Frequency % 1. that-clause 96 76,8 2. presence 2 1,6 3. importance 2 1,6 4. function 1 0,8 5. relationship 1 0,8 6. benefit 1 0,8 7. challenge 1 0,8 8. role 1 0,8 9. utility 1 0,8 10. effect 1 0,8 11. measures 1 0,8 12. processing 1 0,8 13. selection 1 0,8 14. action 1 0,8 15. incidence 1 0,8 16. safety 1 0,8 17. process 1 0,8 18. difference 1 0,8 19. superposition 1 0,8 20. existence 1 0,8 21. rate 1 0,8

45

Table 9 shows that in parallel with the verbs find and show, the verb suggest is

also combined with that-clause more than other collocates (76.8%). Another two

collocates used with the verb suggest are presence and importance constituting 3.2% of

all other collocates (Figure 4.8).

Figure 4.8. Frequency of suggest collocates

It is seen in Figure 4.8. that the verb suggest tends to combine with that-clause

collostruct forming the highest frequency. Other collocates presence and importance in

the health sciences occur with the verb suggest which shows variations combined with

21 different collocates. However, this verb is a strong bias to combine with the that-

clause. The concordance lines of suggest collocates are given in Figure 4.9.

46

Figure 4.9. Concordance lines of suggest collocates

Figure 4.9. Continued Figure

47

Figure 4.9. Continued Figure

Another verb used with different collocates is indicate displaying similarities

with find, show and suggest. The collocates of this verb are shown in Table 10.

48

Table 10

Frequency and Percentages of indicate Collostructs and Collocates

N Collocates Frequency %

1. that 60 50,84

2. role 3 2,54

3. means 3 2,54

4. rate 2 1,69

5. value 2 1,69

6. significance 2 1,69

7. frequency 2 1,69

8. fact 2 1,69

9. whether 1 0.84

10. means 1 0,84

11. orientation 1 0,84

12. sample 1 0,84

13. contribution 1 0,84

14. function 1 0,84

15. relationship 1 0,84

16. involvement 1 0,84

17. pattern 1 0,84

18. risk 1 0,84

19. difference 1 0,84

20. deviation 1 0,84

21. level 1 0,84

22. contrast 1 0,84

23. extent 1 0,84

24. component 1 0,84

25. limit 1 0,84

26. need 1 0,84

27. possibility 1 0,84

28. which 1 0,84

The collocate that-clause accounts for the highest percentage of the verb

indicate (50.84%) and was used 60 times with the verb indicate. The collocates role and

means were used three times with the verb indicate and totally constitute 5.08% of the

49

total collocates. The collocates rate, value, significance, frequency and fact tended to

combine with the verb indicate, each forming 1.69% of the total collocates. These

collocates form 8.45% of the collocates. The most frequently used collocates are given

in Figure 4.10.

Figure 4.10. Frequency of indicate collocates

The collocates role and means accounting for 6% of the total collocates are

strong collocations of the verb indicate. The concordance display of indicate collocates

is given in Figure 4.11.

Figure 4.11. Concordance lines of indicate collocates

50

Continued Figure 4.11.

Figure 4.11. Continued Figure

51

Figure 4.11. Continued Figure

Another verb that has strong collocates is the verb determine combining with 43

individual collocates. The frequency of these collocates is given in Table 11.

52

Table 11

Frequency and Percentages of determine Collostructs and Collocates

N Collocates Frequency % 1. whether/if-clause 41 37,27 2. severity 5 4,54 3. which-clause 3 2,72 4. presence 3 2,72 5. difference 2 1,81 6. outcome 2 1,81 7. stage 2 1,81 8. significance 2 1,81 9. efficacy 2 1,81 10. incidence 2 1,81 11. effect 2 1,81 12. frequency 2 1,81 13. speed 2 1,81 14. rate 2 1,81 15. dependence 2 1,81 16. relationship 1 0,90 17. requirement 1 0,90 18. intensity 1 0,90 19. number 1 0,90 20. associations 1 0,90 21. ability 1 0,90 22. need 1 0,90 23. prevalence 1 0,90 24. intake 1 0,90 25. accessibility 1 0,90 26. adherence 1 0,90 27. location 1 0,90 28. contribution 1 0,90 29. extent 1 0,90 30. influence 1 0,90 31. level 1 0,90 32. predictor 1 0,90 33. mean 1 0,90 34. factor 1 0,90 35. nature 1 0,90 36. dynamics 1 0,90 37. kinetics 1 0,90 38. system 1 0,90 39. score 1 0,90 40. risk 1 0,90 41. feature 1 0,90 42. specificity 1 0,90 43. groups 1 0,90 44. eligibility 1 0,90

53

The most frequently used collocate of the verb determine is that-clause

accounting for 37.27% of the total collocates. The thirteen collocates of the verb

determine form 24.45% of the collocates. The first fourteen collocates together with

that-clause collostruct constitute 61.72% of the list. The collocates forming the highest

percentage are given in Figure 4.12.

Figure 4.12. Frequency of determine collocates

The verb determine is inclined to combine with the collostruct whether-clause

(n=41) and the collocate severity (n=5).The third frequent collostruct which and the

collocate presence each was used three times. Each of the collocates difference,

outcome, stage, significance, efficacy, incidence, effect, speed, rate and dependence was

used twice in the health sciences. The concordancer lines of the verb determine are

given in Figure 4.13.

54

Figure 4.13. Concordance lines of determine collocates

Figure 4.13. Continued Figure

55

Figure 4.13. Continued Figure

Figure 4.13. Continued Figure

Another verb that tended to co-occur with nouns is the verb provide that took 48

collocates. The frequency and percentages of the collocates of provide are given in

Table 12.

56

Table 12

Frequency and Percentage of provide Collostructs and Collocates

N Collocates Frequency % 1. support 13 12,74 2. information 6 5,88 3. evidence 5 4,90 4. input 4 3,92 5. approach 3 2,94 6. clue 3 2,94 7. data 3 2,94 8. care 2 1,96 9. service 2 1,96 10. protection 2 1,96 11. resolution 1 0,98 12. duration 1 0,98 13. index 1 0,98 14. view 1 0,98 15. resolution 1 0,98 16. understanding 1 0,98 17. review 1 0,98 18. source 1 0,98 19. stimulus 1 0,98 20. window 1 0,98 21. agent 1 0,98 22. behaviour 1 0,98 23. constraint 1 0,98 24. conditions 1 0,98 25. imaging 1 0,98 26. assistance 1 0,98 27. counseling 1 0,98 28. nutrition 1 0,98 29. estimates 1 0,98 30. hypothesis 1 0,98 31. hope 1 0,98 32. vision 1 0,98 33. knowledge 1 0,98 34. Status 1 0,98 35. uniformity 1 0,98 36. overview 1 0,98 37. determination 1 0,98 38. recommendation 1 0,98 39. stimulation 1 0,98 40. detail 1 0,98 41. basis 1 0,98 42. ratio 1 0,98 43. description 1 0,98 44. foundation 1 0,98 45. process 1 0,98 46. benefit 1 0,98 47. insight 1 0,98 48. precedent 1 0,98

57

Table 12 indicates that the collocate support accounts for 12.74% of the total

collocates. The collocates information, evidence and input constitute 14.7% of the list.

The first ten collocates account for 42.14% of the total.

Figure 4.14. Frequency of provide collocates

The collocate support used 13 times is the most frequent collocate of the verb

provide. The collocate information used six times was ranked as the second in the list.

The verb provide was used with the collocate evidence five times. Another strong

collocate of this verb was input used four times. The collocates approach, clue and data

were used three times, whereas the collocates care, service and protection were used

twice only. The examples of the verb provide were given in Figure 4.15.

58

Figure 4.15. Concordance lines of provide collocates

Continued Figure 4.15.

59

Continued Figure 4.15.

Continued Figure 4.15.

60

Continued Figure 4.15.

Continued Figure 4.15.

4.2.2. Verbs and Collocates in Physical Science Genre

The verbs used in physical science show some similarities to those in health and

social sciences. The collocates that the same verbs took showed certain variations. The

verbs in physical sciences took more technical collocates. The statistical results of the

verbs that co-occurred with nouns were presented in Table 13.

61

Table 13

Frequency and Percentages of Verbs in the Physical Sciences

No Verbs Frequency Percentage

1. achieve 31 0,96

2. affect 27 0,84

3. alter 8 0,25

4. analyze 15 0,46

5. answer 12 0,37

6. apply 7 0,21

7. assess 35 1,09

8. assign 10 0,31

9. assume 26 0,81

10. avoid 15 0,46

11. become 7 0,21

12. believe 10 0,31

13. calculate 18 0,56

14. capture 18 0,56

15. characterize 8 0,25

16. collect 10 0,31

17. compare 17 0,53

18. comprise 9 0,28

19. conclude 8 0,25

20. consider 66 2,06 21. construct 11 0,34

22. contain 55 1,71

23. continue 7 0,21

24. cover 10 0,31

25. create 11 0,34

26. decrease 12 0,37

27. define 27 0,84

28. demonstrate 10 0,31

29. describe 32 1

30. detect 11 0,34

31. determine 68 2,12 32. develop 17 0,53

62

33. discuss 8 0,25

34. drive 8 0,25

35. eliminate 8 0,25

36. enhance 15 0,46

37. ensure 23 0,71

38. establish 11 0,34

39. estimate 27 0,84

40. evaluate 26 0,81

41. exceed 11 0,34

42. exhibit 24 0,75

43. expect 10 0,31

44. explain 28 0,87

45. explore 8 0,25

46. extend 9 0,28

47. facilitate 14 0,43

48. find 241 7,53

49. follow 17 0,53

50. generate 20 0,62

51. get 16 0,50

52. give 48 1,50

53. identify 22 0,68

54. illustrate 9 0,28

55. imply 8 0,25

56. improve 37 1,15

57. include 66 2,06

58. increase 27 0,84

59. indicate 68 2,12

60. infer 10 0,31

61. influence 16 0,50

62. interpret 9 0,28

63. introduce 8 0,25

64. know 8 0,25

65. let 19 0,59

66. maintain 9 0,28

67. make 56 1,75

63

68. manage 8 0,25

69. meet 14 0,43

70. minimize 8 0,25

71. observe 11 0,34

72. obtain 29 0,90

73. perform 9 0,28

74. predict 25 0,78

75. prevent 12 0,37

76. produce 43 1,34

77. promote 14 0,43

78. propose 10 0,31

79. protect 11 0,34

80. provide 112 3,50

81. reach 11 0,34

82. reduce 28 0,87

83. reflect 20 0,62

84. remove 18 0,56

85. represent 55 1,71

86. reproduce 13 0,40

87. require 30 0,93

88. resemble 11 0,34

89. resolve 11 0,34

90. reveal 19 0,59

91. run 19 0,59

92. say 8 0,25

93. see 23 0,71

94. select 10 0,31

95. show 144 4,50

96. suggest 49 1,53

97. suppose 18 0,56

98. take 19 0,59

99. think 10 0,31

100. validate 8 0,25

64

The verb find, as in the health sciences, is the most frequently used verb in terms

of collocationality. The verbs show and provide are combined with more than 100

collocates. The collocations of the verbs determine and indicate had fewer than 70

occurrences. Collocates of most frequently used verbs were given in Table 14.

Table 14

Frequency and Percentages of find Collostructs and Collocates

N Collocates Frequency % 1. that-clause 51 20,90 2. concentration 19 7,78 3. value 12 4,91 4. species 10 4,09 5. difference 8 3,27 6. result 8 3,27 7. cell 4 1,63 8. level 4 1,63 9. type 4 1,63 10. evidence 4 1,63 11. change 4 1,63 12. correlation 3 1,22 13. efficiency 3 1,22 14. source 3 1,22 15. example 3 1,22 16. detail 2 0,81 17. amount 2 0,81 18. element 2 0,81 19. property 2 0,81 20. proportion 2 0,81 21. way 2 0,81 22. differentiation 1 0,40 23. oscillation 1 0,40 24. image 1 0,40 25. magnitude 1 0,40 26. data 1 0,40 27. specimen 1 0,40 28. cause 1 0,40 29. increase 1 0,40 30. reduction 1 0,40 31. probability 1 0,40 32. obstacle 1 0,40 33. case 1 0,40 34. recovery 1 0,40 35. test 1 0,40

65

36. agreement 1 0,40 37. life stage 1 0,40 38. persistence 1 0,40 39. feature 1 0,40 40. particle 1 0,40 41. trajectory 1 0,40 42. application 1 0,40 43. effect 1 0,40 44. deviation 1 0,40 45. model 1 0,40 46. weight 1 0,40 47. variation 1 0,40 48. response 1 0,40 49. study 1 0,40 50. sediment 1 0,40 51. ratio 1 0,40 52. rate 1 0,40 53. dependence 1 0,40 54. substance 1 0,40 55. error peak 1 0,40 56. relation 1 0,40 57. support 1 0,40 58. equilibrium 1 0,40 59. concept 1 0,40 60. form 1 0,40 61. gain 1 0,40 62. explanation 1 0,40 63. structure 1 0,40 64. design 1 0,40 65. connection 1 0,40 66. how 1 0,40 67. variable 1 0,40

The collocate that-clause accounts for 20.90% of the total collocates. The

collocate concentration forms 7.78% of the collocates. Another frequent collocate is

value making up 4.91% of the total collocates. The first eleven collocates accounted for

52.37% of all collocates, and the most frequent ones were given in Figure 4.16.

66

Figure 4.16. Frequency of find collocates

The collostruct that-clause was used at the highest frequency with 51 instances.

The most frequent collocate concentration was used 19 times. Another collocate used

was value with 12 times. The collocate species was used 10 times. The collocates result

and difference were used eight times. The collocates cell, level, type, evidence and

change were used four times. The collocates correlation, efficiency, source and example

were used 3 times. The examples of the verb find were presented in Figure 4.17.

Figure 4.17. Concordance lines of find collocates

67

Continued Figure 4.17.

Continued Figure 4.17.

Another frequent verb used is show co-occurring with 54 collocates. The verb

show was used in three academic genres. The frequency and percentages of the verb

show are given in Table 15.

68

Table 15

Frequency and Percentages of show Collostructs and Collocates

N Collocates Frequency % 1. that-clause 59 40,97 2. velocity 6 4,16 3. value 5 3,47 4. model 4 2,77 5. increase 3 2,08 6. association 3 2,08 7. difference 3 2,08 8. coverage 3 2,08 9. importance 2 1,38 10. reduction 2 1,38 11. pattern 2 1,38 12. variation 2 1,38 13. shift 2 1,38 14. change 2 1,38 15. thickness 2 1,38 16. deviation 2 1,38 17. evidence 2 1,38 18. activity 2 1,38 19. relationship 2 1,38 20. view 2 1,38 21. value 1 0,69 22. depth 1 0,69 23. response 1 0,69 24. range 1 0,69 25. variability 1 0,69 26. deposit 1 0,69 27. decline 1 0,69 28. periodicity 1 0,69 29. correlation 1 0,69 30. reaction 1 0,69 31. factor 1 0,69 32. agreement 1 0,69 33. comparison 1 0,69 34. concentration 1 0,69 35. level 1 0,69 36. nature 1 0,69 37. clustering 1 0,69 38. diameter 1 0,69 39. patch 1 0,69

69

40. error 1 0,69 41. influence 1 0,69 42. drift 1 0,69 43. sign 1 0,69 44. distribution 1 0,69 45. similarity 1 0,69 46. bias 1 0,69 47. experiment 1 0,69 48. result 1 0,69 49. lack 1 0,69 50. directionality 1 0,69 51. validity 1 0,69 52. absorption 1 0,69 53. location 1 0,69 54. effect 1 0,69

Table 15 shows that the most frequently used collocate was that-clause

accounting for 40.97% of the collocates. The percentage of the collocate velocity is

4.16%. Another collocate used is value with 3.47%. The collocate model accounts for

2.77% of all collocates. The first four collocates of the verb show form 51.37% of the

list. The most frequently used collocates were given in Figure 4.18.

Figure 4.18. Frequency of show collocates

70

The collocates velocity and value were used six and five times respectively. The

collocate model was used four times. The collocates increase, association, difference

and coverage were used three times. The collocates importance, reduction, pattern,

variation, shift, change, thickness, deviation, evidence, activity, relationship and view

each were used only twice. Some selected examples from the concordance are given in

Figure 4.19.

Figure 4.19. Concordance lines of show collocates

Continued Figure 4.19.

71

Continued Figure 4.19.

Continued Figure 4.19.

Another frequently used verb is provide with 112 collocations. The frequency

and percentages of this verb were presented in Table 16.

72

Table 16

Frequency and Percentages of provide Collostructs and Collocates

N Collocates Frequency % 1. information 18 16,07 2. tool 7 6,25 3. evidence 6 5,35 4. data 6 5,35 5. way 4 3,57 6. basis 3 2,67 7. framework 3 2,67 8. insight 2 1,78 9. correlation 2 1,78 10. review 2 1,78 11. update 2 1,78 12. measure 2 1,78 13. constraint 2 1,78 14. flux 2 1,78 15. condition 1 0,89 16. review 1 0,89 17. answer 1 0,89 18. dataset 1 0,89 19. velocity 1 0,89 20. alternative 1 0,89 21. measurement 1 0,89 22. perception 1 0,89 23. field 1 0,89 24. record 1 0,89 25. route 1 0,89 26. expression 1 0,89 27. review 1 0,89 28. story 1 0,89 29. perspective 1 0,89 30. time 1 0,89 31. gain 1 0,89 32. incentive 1 0,89 33. understanding 1 0,89 34. indication 1 0,89 35. database 1 0,89 36. clue 1 0,89 37. detail 1 0,89 38. response 1 0,89 39. spectra 1 0,89 40. condition 1 0,89

73

41. venue 1 0,89 42. food 1 0,89 43. image 1 0,89 44. habitat 1 0,89 45. efficiency 1 0,89 46. housing 1 0,89 47. size 1 0,89 48. launch 1 0,89 49. functionality 1 0,89 50. activity 1 0,89 51. protection 1 0,89 52. indicator 1 0,89 53. sardine 1 0,89 54. assistance 1 0,89 55. compromise 1 0,89 56. discrimination 1 0,89 57. average 1 0,89 58. flow 1 0,89 59. incentive 1 0,89 60. coordination 1 0,89 61. voltage 1 0,89 62. fuel 1 0,89 63. access 1 0,89 64. characteristics 1 0,89 65. vacuum 1 0,89 66. perspective 1 0,89

Table 16 indicates that the collocate information forms the highest percentage

with 16.07. The percentage of the collocate tool is 6.25. The collocates evidence and

data each forms 5.35% of the total collocates. The first fourteen collocates account for

54.39% of all collocates. The most frequently used collocates were given in Figure 4.20.

74

Figure 4.20. Frequency of provide collocates

The most frequent collocates information and tool were used 18 and 7 times

respectively. The collocates evidence and data and way were used six and five times

respectively. The collocates basis and frame were used four times. The collocates

insight, correlation, review, update, measure, constraint and flux each were used twice.

Examples of the collocates of the verb provide were given in Figure 4.21.

Figure 4.21. Concordance lines of provide collocates

75

Continued Figure 4.21.

Continued Figure 4.21.

76

Continued Figure 4.21.

Continued Figure 4.21.

77

The verb determine combined with 43 collocates was shown in Table 17.

Table 17

Frequency and Percentages of determine Collostructs and Collocates

N Collocates Frequency %

1. whether/if 18 24,65

2. distribution 4 5,47

3. composition 4 5,47

4. property 3 4,10

5. concentration 3 4,10

6. date 2 2,73

7. difference 2 2,73

8. state 2 2,73

9. amount 2 2,73

10. mechanism 2 2,73

11. what 1 1,36

12. source 1 1,36

13. model 1 1,36

14. factor 1 1,36

15. average 1 1,36

16. how 1 1,36

17. parameter 1 1,36

18. intake 1 1,36

19. content 1 1,36

20. mass 1 1,36

21. phase 1 1,36

22. length 1 1,36

23. prey 1 1,36

24. acceleration 1 1,36

25. characteristics 1 1,36

26. system 1 1,36

27. error 1 1,36

28. gain 1 1,36

78

29. level 1 1,36

30. percentage 1 1,36

31. variation 1 1,36

32. number 1 1,36

33. component 1 1,36

34. motion 1 1,36

35. polarization 1 1,36

36. probability 1 1,36

37. residue 1 1,36

38. time 1 1,36

39. uncertainty 1 1,36

40. visibility 1 1,36

41. volume 1 1,36

42. phase 1 1,36

43. thickness 1 1,36

44. average 1 1,36

The collocate whether was used mostly with the verb determine. The collocates

distribution and composition account for 10.94% of the total collocates. The first five

collocates form 43.79 % of all collocates in the distribution. The most frequent

collocates were given in Figure 4.22

Figure 4.22. Frequency of determine collocates

79

The collocates distribution and composition were used four times: property and

concentration three times, and date, difference, state, amount and mechanism each

twice. Examples of these collocates were given in Figure 4.23.

Figure 4.23. Concordance lines of determine collocates

Continued Figure 4.23.

80

Continued Figure 4.23.

Continued Figure 4.23.

Another verb frequently used is indicate co-occurring with 25 collocations.

Frequency and percentages of this verb were presented in Table 18.

81

Table 18

Frequency and Percentages of indicate Collostructs and Collocates

N Collocates Frequency %

1. that-clause 36 52,94

2. error 5 7,35

3. difference 4 5,88

4. level 2 2,94

5. location 1 1,47

6. correlation 1 1,47

7. potential 1 1,47

8. effect 1 1,47

9. gradient 1 1,47

10. contamination 1 1,47

11. origin 1 1,47

12. change 1 1,47

13. environment 1 1,47

14. change 1 1,47

15. variation 1 1,47

16. level 1 1,47

17. location 1 1,47

18. concentration 1 1,47

19. peach 1 1,47

20. strata 1 1,47

21. diversity 1 1,47

22. profile 1 1,47

23. presence 1 1,47

24. range 1 1,47

25. whether-clause 1 1,47

Table 18 indicates that the most frequently used collocate is that-clause with

52.94%: and the second is error with 7.35%. The first four collocations account for

69.11% of the total collocates. The most frequent collocates were given in Figure 4.24.

82

Figure 4.24. Frequency of indicate collocates

The most frequently used collocates error was observed five times: difference

four times: level twice: location, correlation, potential, effect, gradient, contamination

and origin only once. Examples of the collocates of indicate were given in Figure 4.25.

Figure 4.25. Concordance lines of indicate collocates

83

Continued Figure 4.25.

Continued Figure 4.25.

4.2.3. Verbs and Collocates in the Social Sciences

In order to balance the sampling, a similar number of words were targeted in the

social sciences as well. The first hundred verbs were chosen to see the frequency and

84

percentages. The verbs in the social sciences show several similarities to those in the

health and physical sciences. However, in terms of the rank, there exist some variations.

The results showed that the most commonly used verb in the social sciences as well was

find.

Table 19

Frequency and Percentages of Verbs in the Social Sciences

N Verbs Frequency % 1. accept 24 0,33 2. achieve 42 0,58 3. address 45 0,62 4. adopt 40 0,55 5. affect 34 0,47 6. allow 48 0,66 7. alter 33 0,45 8. apply 38 0,52 9. argue 75 1,04 10. ask 26 0,36 11. assess 44 0,61 12. assume 30 0,41 13. avoid 37 0,51 14. become 53 0,73 15. believe 57 0,79 16. bring 22 0,3 17. capture 22 0,3 18. challenge 34 0,47 19. conclude 21 0,29 20. consider 80 1,11 21. constitute 19 0,26 22. contribute 36 0,49 23. create 56 0,77 24. define 21 0,29 25. describe 37 0,51 26. determine 51 0,7 27. develop 74 1,02 28. discuss 48 0,66 29. encourage 24 0,33 30. engage 61 0,84 31. ensure 37 0,51 32. establish 26 0,36 33. evaluate 22 0,3

85

34. examine 56 0,77 35. expect 40 0,55 36. explain 68 0,94 37. explore 29 0,4 38. express 40 0,55 39. find 230 3,19 40. follow 19 0,26 41. gain 35 0,48 42. get 35 0,48 43. give 59 0,81 44. help 52 0,72 45. highlight 22 0,3 46. hold 40 0,55 47. identify 54 0,74 48. imply 21 0,29 49. improve 46 0,63 50. include 79 1,09 51. increase 34 0,47 52. indicate 44 0,61 53. influence 46 0,63 54. interpret 25 0,34 55. keep 27 0,37 56. know 55 0,76 57. lead 68 0,94 58. leave 22 0,3 59. let 22 0,3 60. maintain 29 0,4 61. make 163 2,26 62. mean 30 0,41 63. need 22 0,3 64. note 22 0,3 65. offer 22 0,3 66. overcome 24 0,33 67. participate 54 0,74 68. perceive 20 0,27 69. present 30 0,41 70. prevent 21 0,29 71. produce 40 0,55 72. promote 29 0,40 73. provide 87 1,34 74. read 58 0,80 75. realize 24 0,33 76. receive 22 0,30

86

77. recognize 55 0,76 78. reduce 32 0,44 79. refer 37 0,51 80. reflect 22 0,30 81. relate 26 0,36 82. report 24 0,33 83. represent 39 0,54 84. require 39 0,54 85. respond 34 0,47 86. reveal 20 0,27 87. say 41 0,56 88. see 74 1,02 89. seek 22 0,30 90. show 89 1,23 91. suggest 55 0,76 92. support 31 0,43 93. take 77 1,06 94. teach 26 0,36 95. tell 40 0,55 96. test 23 0,31 97. think 70 0,97 98. try 32 0,44 99. understand 108 1,49

100. use 55 0,76

The descriptive results in Table 19 show that the verb find accounts for the

highest percentage (3.19%). The first most frequently used fourteen verbs form 21.39%

of the total verbs on the list. The percentages of these verbs range from 3.19 to 1.02.

The percentages of the first most frequently used three verbs (find, make and see) are

over 2, whereas the percentages of the verbs (take, know, use) is over 2.25 in total. The

verbs provide and suggest account for 2.1% of the total verbs in the social science

research articles. The percentage of the verb show is only 1.23%. The verbs consider,

include, argue and develop form 1.11%, 1.09%, 1.04 %, 1.02% of the total verbs

respectively. The first six verbs (find, make, see, take, know and use) are used with over

50 collocates. In accordance with the results of the verbs in the social sciences, it is

important to show the collocates of the first five verbs. The collocates of the most

frequently used verb find were shown in Table 20.

87

Table 20

Frequency and Percentages of find Collostructs and Collocates

N Collocates Frequency %

1. that –clause 106 15,89

2. benefits 17 2,54

3. difference 10 1,49

4. evidence 8 1,19

5. way 6 0,89

6. interaction 5 0,74

7. effect 4 0,59

8. report 3 0,44

9. correlation 3 0,44

10. example 3 0,44

11. consideration 2 0,29

12. factor 2 0,29

13. association 2 0,29

14. explanation 2 0,29

15. expression 2 0,29

16. meaning 2 0,29

17. study 2 0,29

18. support 2 0,29

19. satisfaction 2 0,29

20. provision 1 0,14

21. link 1 0,14

22. mean 1 0,14

23. role 1 0,14

24. variation 1 0,14

25. idea 1 0,14

26. rationale 1 0,14

27. variation 1 0,14

28. resource 1 0,14

29. consistency 1 0,14

30. change 1 0,14

88

31. concept 1 0,14

32. insight 1 0,14

33. instances 1 0,14

34. imposition 1 0,14

35. resolution 1 0,14

36. rule 1 0,14

37. result 1 0,14

38. deviation 1 0,14

39. relationship 1 0,14

40. distinction 1 0,14

41. aspect 1 0,14

42. justification 1 0,14

43. element 1 0,14

44. balance 1 0,14

45. account 1 0,14

46. position 1 0,14

47. array 1 0,14

48. sample 1 0,14

49. principle 1 0,14

50. technique 1 0,14

51. rudiment 1 0,14

52. choice 1 0,14

53. link 1 0,14

54. account 1 0,14

55. record 1 0,14

56. positivity 1 0,14

57. dependence 1 0,14

58. solution 1 0,14

59. clue 1 0,14

60. move 1 0,14

61. means 1 0,14

62. argument 1 0,14

89

The verb find harbors the highest variation with its 62 collocates. The

interpretative aspect of social science may have produced this variation. 15.89% of the

collocates was made up of the collocate that-clause. The collocate benefit forms 2.54%

of the total collocates. Another frequently used collocate with the verb find is the word

difference with 1.49%. The most frequent collocates are given in Figure 4.26

Figure 4.26. Frequency of find collocates

Figure 4.26. shows that the most frequent collocate benefit was used 17 times.

Another strong collocate is difference that was used 10 times. The collocate evidence

was used 8 times. The collocates way and interaction were used six and five times

respectively. Another important collocate of the verb find was effect that was used four

times. The collocates of report, correlation and example were used three times each.

The collocates consideration, factor, association, explanation, expression, meaning,

study, support and satisfaction each were used only twice. The first ten collocates

account for 24.65 % of the total collocates. The examples of find from the concordance

are given in Figure 4.27.

90

Figure 4.27. Concordance lines of find collocates

Continued Figure 4.27.

91

Continued Figure 4.27.

Continued Figure 4.27.

92

Continued Figure 4.27.

Continued Figure 4.27.

Another frequently used verb is understand combined with 58 collocates. The

variation of the verb understand may be based on the interpretative nature of social

sciences. The frequency and percentages of the verb understand are given in Table 21.

93

Table 21

Frequency and Percentages of understand Collostructs and Collocates

N Collocates Frequency %

1. that-clause 24 22,22

2. structure 12 11,11

3. object 3 2,77

4. way 3 2,77

5. world 3 2,77

6. use 2 1,85

7. context 2 1,85

8. dynamics 2 1,85

9. significance 2 1,85

10. role 1 0,92

11. achievement 1 0,92

12. role 1 0,92

13. what 1 0,92

14. concept 1 0,92

15. how 1 0,92

16. matter 1 0,92

17. belief 1 0,92

18. relation 1 0,92

19. motivation 1 0,92

20. difference 1 0,92

21. practices 1 0,92

22. justice 1 0,92

23. text 1 0,92

24. source 1 0,92

25. attitude 1 0,92

26. statement 1 0,92

27. opinion 1 0,92

28. answer 1 0,92

29. difference 1 0,92

30. extent 1 0,92

94

31. framework 1 0,92

32. impact 1 0,92

33. inequality 1 0,92

34. issue 1 0,92

35. language 1 0,92

36. trajectory 1 0,92

37. particularity 1 0,92

38. trust 1 0,92

39. questionablessness 1 0,92

40. condition 1 0,92

41. process 1 0,92

42. role 1 0,92

43. salience 1 0,92

44. situation 1 0,92

45. knowledge 1 0,92

46. relationship 1 0,92

47. view 1 0,92

48. level 1 0,92

49. prediction 1 0,92

50. mechanism 1 0,92

51. opinion 1 0,92

52. whether 1 0,92

53. why 1 0,92

54. culture 1 0,92

55. behavior 1 0,92

56. nature 1 0,92

57. function 1 0,92

58. justice 1 0,92

Table 21 shows that the most frequently collocate is that-clause accounting for

22.22% of total collocates. The collocate structure forms 11.11% of all collocates. It

can be reasoned that the first two constitute 33.33% of the total. More interestingly, the

first nine words account for 49.04% of total collocates, which shows that some

95

collocates tend to co-occur more frequently with other verbs. The most frequent

collocates are given in Figure 4.28.

Figure 4.28. Frequency of understand collocates

The most frequent collocate structure was used 12 times as indicated in Figure

4.28. The collocations object, way, world each were used three times, while the

collocates use, context, dynamics and significance were used twice. Selected examples

of understand are given in Figure 4.29.

Figure 4.29. Concordance lines of understand collocates

96

Continued Figure 4.29.

Continued Figure 4.29.

97

Continued Figure 4.29.

Another frequently used verb is make with its 50 collocates. This verb is quite

distinctive with its various usages of collostructions as well as collocates

98

Table 22

Frequency and Percentages of make Collostructs and Collocates

N Collocates Frequency %

1. decision 17 10,42

2. make sth adjective 17 10,42

3. make sbd adjective 13 7,97

4. make sbd adjective 13 7,97

5. sense 9 5,52

6. choice 9 5,52

7. make sth do 7 4,29

8. make sbd do 6 3,68

9. make sth sth 6 3,68

10. change 5 3,06

11. contribution 5 3,06

12. use 4 2,45

13. make sbd sbd 3 1,84

14. effort 3 1,84

15. time 3 1,84

16. make sbd done 2 1,22

17. make sth done 2 1,22

18. make sth into 2 1,22

19. distinction 2 1,22

20. argument 2 1,22

21. representation 2 1,22

22. point 2 1,22

23. system 2 1,22

24. prediction 2 1,22

25. pleasure 2 1,22

26. claim 2 1,22

27. make sbd sth 1 0,61

28. promise 1 0,61

29. relation 1 0,61

30. assumption 1 0,61

99

31. difference 1 0,61

32. living 1 0,61

33. determination 1 0,61

34. rule 1 0,61

35. attempt 1 0,61

36. demand 1 0,61

37. guess 1 0,61

38. accusation 1 0,61

39. adjustment 1 0,61

40. discrimination 1 0,61

41. fun 1 0,61

42. progress 1 0,61

43. contract 1 0,61

44. destiny 1 0,61

45. case 1 0,61

46. interaction 1 0,61

47. meaning 1 0,61

48. judgment 1 0,61

49. law 1 0,61

50. link 1 0,61

51. love 1 0,61

52. payment 1 0,61

53. room 1 0,61

54. connection 1 0,61

55. enquiry 1 0,61

56. assertion 1 0,61

57. investment 1 0,61

58. commitment 1 0,61

59. schedule 1 0,61

60. arrangement 1 0,61

61. criticism 1 0,61

100

Table 22 shows that decision accounts for 16.34% of all collocates. The high

frequency of the collocate decision might be a striking feature of social sciences

because the collocate decision was not seen in health or physical sciences. The

collocates sense and choice account for 17.30% of the total collocates. The collocations

change and contribution constitute 9.60% of the collocates. The first sixteen collocates

account for 64.36% of total collocates. The most frequently used collocates are given in

Figure 4.18.

Figure 4.30. Frequency of make collocates

Figure 4.30. shows that the most frequent collocate decision was used 17 times.

The collocates sense and choice were used nine times; change and contribution five

times; use four times; effort and time three times; distinction, argument, representation,

point, system, prediction, pleasure and calm only twice. The selected examples of the

verb make obtained from the concordance are given in Figure 4.31.

101

Figure 4.31. Concordance lines of make collocates

Continued Figure 4.31.

102

Continued Figure 4.31.

Continued Figure 4.31.

103

Continued Figure 4.31.

Another frequently used verb is provide producing a great number of variations.

It co-occurs with 60 collocates. The first four verbs in social sciences co-occur with

more various collocates. The frequency and percentages of provide are given in Table

23.

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Table 23

Frequency and Percentages of provide Collostructs and Collocates

N Collocates Frequency %

1. somebody 6 6,89

2. opportunity 5 5,74

3. insight 4 4,59

4. reason 4 4,59

5. guidance 4 4,59

6. support 3 3,44

7. evidence 3 3,44

8. information 3 3,44

9. tool 3 3,44

10. relief 2 2,29

11. ground 2 2,29

12. glimpse 2 2,29

13. framework 2 2,29

14. context 2 2,29

15. resource 2 2,29

16. solution 2 2,29

17. leadership 2 2,29

18. loan 1 1,14

19. perspective 1 1,14

20. service 1 1,14

21. basis 1 1,14

22. follow-up 1 1,14

23. surety 1 1,14

24. baseline 1 1,14

25. understanding 1 1,14

26. appraisal 1 1,14

27. case 1 1,14

28. summary 1 1,14

29. foundation 1 1,14

30. breadth 1 1,14

105

31. myriad 1 1,14

32. perspective 1 1,14

33. basis 1 1,14

34. connection 1 1,14

35. reassurance 1 1,14

36. money 1 1,14

37. precedent 1 1,14

38. clue 1 1,14

39. consciousness 1 1,14

40. analysis 1 1,14

41. focus 1 1,14

42. assessment 1 1,14

43. detail 1 1,14

44. condition 1 1,14

45. experience 1 1,14

46. means 1 1,14

47. project 1 1,14

48. response 1 1,14

49. rule 1 1,14

50. stability 1 1,14

51. production 1 1,14

52. protection 1 1,14

53. goal 1 1,14

54. strategy 1 1,14

55. technique 1 1,14

56. affiliation 1 1,14

57. picture 1 1,14

58. guideline 1 1,14

59. source 1 1,14

60. way 1 1,14

106

Table 23 indicates that the collocate somebody accounts for 6.89% of total

collocates. Another frequently used collocation is opportunity forming 5.74% of total

collocates. The percentages of insight and reason are 4.59% each. The first four

collocations constitute 21.81% of total collocates. The first eighteen collocates account

for 58.47% of all collocates.

Figure 4.32. Frequency of provide collocates

Figure 4.32 shows that the most frequent collocate somebody was used six times.

The collocate opportunity was used five times: insight reason and guidance four times:

support, evidence, information and tool three times: and relief, ground, and glimpse

twice. The examples from the concordance are given in Figure 4.33.

107

Figure 4.33. Concordance lines of provide collocates

Continued Figure 4.33.

108

Continued Figure 4.33.

Continued Figure 4.33.

109

Another important verb is suggest with 18 collocations. The frequency and percentages

of this verb are given in Table 24.

Table 24

Frequency and Percentages of suggest Collostructs and Collocates

N Collocates Frequency %

1. that 74 81,31

2. advantage 2 2,19

3. possibility 2 2,19

4. association 1 1,09

5. interpretation 1 1,09

6. belief 1 1,09

7. failure 1 1,09

8. deferral 1 1,09

9. movement 1 1,09

10. how 1 1,09

11. improvement 1 1,09

12. strategy 1 1,09

13. absence 1 1,09

14. resource 1 1,09

15. value 1 1,09

16. capacity 1 1,09

17. why 1 1,09

110

Figure 4.34. Frequency of suggest collocates

Figure 4.35. Concordance lines of suggest collocates

111

Continued Figure 4.35.

The results of the three genres need to be compared so that a general overview of

the data can be understood better with descriptive statistics and chi-square analysis. The

common verbs extracted from the data presented above will be given and compared in

the next chapter.

4.2.4. Comparison of the Results

The academic genres were compared considering the common verbs extracted

from the corpora. The results were obtained through descriptive statistics. The number

of the common verbs across the three genres was 165. The percentages and ratio of the

common verbs in proportion to total verbs between and across academic genres are

given in Table 25.

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Table 25

Descriptive Statistics of Common Verbs across the Three Genres

Academic genres Collocational verbs Common verbs %

Health sciences 714 165 23.10

Physical sciences 556 165 29.67

Social sciences 920 165 17.93

Total 2190 495 22.60

Table 25 shows that common verbs used in the three genres composing 22.60%

of total verbs, which imply that 77.40% of all are different from each other. The ratio of

common verbs in the health sciences is 23.10%; physical sciences, 29.67%, and social

sciences, 17.93%. Since the verbs and their collocates in the social sciences showed

more variations, the ratio of common verbs was lower than those of health and physical

sciences. In order to look into the relationship and significance level of the 165 common

verbs across the three genres, a chi square analysis was conducted.

Table 26

Overall Results of the Chi-Square Analysis of the Common Verbs in Three Academic

Genres

Academic Genres n value df p

Health-physical sciences 165 628 164 .000

Health–social sciences 165 1307 164 .000

Physical-social sciences 165 912 164 .000

Table 26 shows that there is a significant difference between the 165 verbs

across the health, physical and social sciences (p<0.00). Hyland (2004) argues that each

academic discipline is unique in that it uses different textual collocations. In line with

this observation, our results support Hyland’s argument, although similar verbs are used

across the three genres. The similarity is, however, more striking between the health and

physical sciences. The social sciences showed more variation in all data. However, the

descriptive statistics of each verb through frequency analysis is given in Table 27 in

order to understand the value of each verb in the data.

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Table 27

Descriptive Statistics of Each Common Verb

Verbs Health

sciences

Physical

sciences

Social

sciences

1. absorb 1 4 1

2. achieve 15 31 42

3. acquire 4 3 9

4. activate 29 1 5

5. add 2 7 9

6. adjust 2 3 4

7. adopt 17 2 40

8. advocate 2 1 3

9. affect 58 27 34

10. alter 13 8 33

11. analyze 12 15 13

12. apply 8 7 38

13. appreciate 1 4 10

14. argue 2 6 75

15. assess 68 35 44

16. assist 2 1 9

17. assume 12 26 30

18. become 18 7 53

19. believe 21 10 57

20. bring 5 7 22

21. calculate 5 18 4

22. capture 10 18 22

23. categorize 3 2 7

24. clarify 13 3 6

25. collect 7 10 2

26. colonize 2 3 1

27. combine 2 6 5

28. compare 46 17 9

29. comprise 3 9 3

114

30. compute 2 5 1

31. conclude 13 8 21

32. conduct 7 3 15

33. confirm 29 7 15

34. conserve 1 4 16

35. consider 26 66 80

36. constitute 6 7 19

37. contain 29 55 11

38. continue 21 7 8

39. convey 1 3 6

40. define 17 27 21

41. deliver 2 3 4

42. depict 4 1 2

43. describe 25 32 37

44. detect 70 11 1

45. determine 110 68 51

46. develop 31 17 74

47. discover 2 1 14

48. discuss 9 8 48

49. display 26 4 7

50. disturb 1 2 1

51. dominate 3 7 8

52. eliminate 8 8 7

53. emphasize 8 6 8

54. employ 6 1 10

55. enable 4 7 10

56. encourage 2 2 24

57. enhance 25 15 15

58. ensure 27 23 37

59. entail 2 1 7

60. enter 3 11 5

61. envision 1 2 5

62. establish 16 11 26

115

63. examine 43 6 56

64. exceed 6 11 5

65. exclude 24 1 4

66. exert 17 2 1

67. exhibit 25 24 10

68. explain 39 28 68

69. explore 22 8 29

70. expose 3 1 1

71. extend 7 9 9

72. find 311 241 230

73. follow 24 17 19

74. fulfill 9 3 7

75. generate 24 20 13

76. get 15 16 35

77. give 19 48 59

78. guarantee 3 6 3

79. handle 3 1 9

80. highlight 7 7 22

81. hinder 2 1 3

82. identify 41 22 54

83. illustrate 10 9 6

84. imagine 3 2 18

85. implement 12 6 9

86. implicate 2 1 1

87. imply 9 8 21

88. improve 54 37 46

89. include 92 66 79

90. indicate 118 68 44

91. infer 4 10 4

92. inform 1 5 9

93. inhibit 56 4 3

94. initiate 8 2 4

95. interpret 2 9 25

116

96. introduce 3 8 7

97. involve 18 7 19

98. justify 2 2 19

99. keep 8 4 27

100. know 13 8 55

101. learn 5 5 10

102. leave 3 4 22

103. locate 1 4 9

104. maintain 28 9 29

105. make 33 56 163

106. match 5 7 3

107. maximize 3 2 2

108. meet 7 14 18

109. mention 2 5 2

110. modify 10 5 4

111. observe 14 11 9

112. obtain 31 29 5

113. offer 11 6 22

114. overcome 6 5 24

115. perceive 2 1 20

116. perform 25 9 17

117. permit 5 7 7

118. predict 22 25 17

119. prepare 1 5 4

120. prevent 46 12 21

121. produce 38 43 40

122. promote 25 14 29

123. propose 5 10 8

124. protect 5 11 12

125. prove 3 5 6

126. provide 102 112 87

127. raise 9 4 11

128. reach 9 11 10

117

129. realize 1 5 32

130. receive 32 1 22

131. recognize 16 4 55

132. reduce 56 28 32

133. refine 2 1 4

134. reflect 33 20 22

135. regulate 6 3 7

136. remember 6 5 12

137. remove 15 18 4

138. replace 1 3 6

139. represent 67 55 39

140. require 36 30 39

141. resist 8 1 10

142. resolve 7 11 3

143. restrict 2 6 6

144. retain 9 4 8

145. retrieve 2 5 10

146. reveal 15 19 20

147. satisfy 1 5 7

148. say 1 8 41

149. see 2 23 74

150. shed 3 1 9

151. show 133 144 89

152. solve 2 6 6

153. specify 2 4 3

154. stimulate 9 1 5

155. suggest 125 49 55

156. summarize 4 6 1

157. sustain 5 1 3

158. take 23 19 77

159. think 5 10 70

160. treat 11 1 16

161. undergo 20 1 3

118

162. undertake 2 1 1

163. utilize 3 3 4

164. validate 2 8 2

165. verify 5 5 2

Table 27 indicates that the details of each genre and verb give a clearer idea

regarding the data. It can be observed that 35.15% of the verbs in the list did not show a

close relation. Therefore, it can be said that 64.85% of them can be commonly used

across the three genres. 18.18% of the verbs did not show an important relationship

between the physical-social and health-social sciences, which indicates that there was a

slight relationship between the social and the other two sciences. 9% of the verbs

showed a significant relationship between the health and physical sciences. In other

words, 91% of the verbs showed a strong relationship between the health and physical

sciences. It can be concluded that the social sciences differed considerably in terms of

the collocates the verbs attracted. 57% of the verbs showed commonalities because

there was a strong relationship between 95 verbs. Since there was a strong relationship

between the health and physical sciences, it is important to show the most common

seven verbs used more than 100 instances in the data and to compare them in terms of

the percentage in Table 28.

Table 28

Percentages of Most Common Verbs in the Health and Physical Sciences

Verbs Health sciences

%

Physical sciences

%

find 311 241

show 133 144

represent 67 55

determine 110 68

provide 102 112

include 92 66

indicate 118 68

Total 831 754

119

Table 28 shows that a total percentage of the verbs co-occurring with collocates

are almost similar. Since these two genres are often characterized as ‘hard sciences’

(Swales, 2004; Hyland, 2004, 2009; Biber, 2006), it is interpretable that similar verbs

with their collocates were used for both. It is natural that the social sciences, covering

‘soft disciplines’, use more various verbs and collocations compared to the other two

genres. The chi-square analysis of the seven verbs is given in Table 28.

Table 29

Results of the Chi-Square Analysis of the Common Verbs in the Health and Physical

Sciences

Academic genres n value df P

Health-physical sciences 7 35.000 30 .243

Table 29 shows that there is no significant difference between the health and

physical sciences (p<.243). As the frequency showed an increase between the verbs, the

relationship between these verbs indicated a commonality and similarity. From Table 28

and 29, it can be deduced that when the number of the common verbs increased, the

relationship between the verbs decreased, and that when only the most frequently verbs

were computed, the relationship showed a strong similarity. As the number of the verbs

varied, the relationship decreased, which can be interpreted that sciences show

variations in the use of collocations with similar verbs, and each science, particularly

the social sciences, produces its own idiosyncratic collocations and collostructions.

4.3. Results Related to Research Question (2)

Research question 2: How are these lexical collocations (collostructions)

constructed from a constructionist grammar view?

Collocations are not only made up of simple co-occurring words but also refer to

some grammatical properties, and eventually form collostructions (Gries &

Stefanowitsch, 2004; Gries, 2011; Goldberg, 2011). The constructionist grammar view

does not create a dichotomy between lexis and syntax in language. Hoey (2005) argues

that ‘each word is primed to occur with particular words, semantic sets, pragmatic

functions and grammatical positions’ (p.13). In this sense, grammar and lexis are placed

on a continuum (to introduce the term ‘gramlex’) and collocations are not exceptions.

120

The nature of collocations varies in various social contexts. The main emphasis in

collocation studies based on corpus research has been linear (Gries and Stefanowitsch,

2004). Extending collocations into constructions or collostructions can provide a

holistic and integrative approach. Pattern grammar (Hunston and Francis, 2000) as

well as construction grammar also refers to the fact that lexis and grammar are not

different from each other. Gries and Stefanowitsch (2003, p. 215) describe construction

and collostructions operationally:

Lexemes that are attracted to a particular construction are referred to as collexemes of

this construction; conversely, a construction associated with a particular lexeme may be referred

to as a collostruct; the combination of a collexeme and a collostruct will be referred to as a

collostruction.

In traditional studies of collocations, only two lexical components are given and

ranked in order (Gries & Stefanowitsch, 2003). Specific constructions related to the

verbs chosen are often ignored, and thus regarded as collocations. Rather, these

constructions are perceived as grammatical only. According to the traditional view,

grammatical constructions cannot be collocations since collocations are assumed to

refer to only naive and simple content words. However, with this study we dealt with

strongly attracted collostructions as well as collocations. The first five frequently used

verbs and their collostructions will be discussed from a constructionist grammar

perspective.

4.3.1. Collostructions in the Health Sciences

Examining the constructions of several lexical verbs in detail brings about an

additional dimension to the analysis of collocations. The fact that collocations are not

merely simple words presents a holistic view. The most frequent five verbs are given in

Table 30

121

Table 30

Distribution of Most Five Frequent Verbs in the Health Sciences

Verbs Frequency %

find 311 7.52

show 133 3,21

suggest 125 3,02

indicate 118 2,85

determine 110 2,66

Table 30 shows that each verb occurred with different collostructs more than

100 instances. The detailed analysis of each verb is given in Table 31.

Table 31

Frequency and Percentages of Collostructs in the Health Sciences

Verbs Collostruct Frequency %

find that 92 29.58

show that 44 33.08

suggest that 96 76.80

indicate that 60 50.84

determine whether 41 37.27

Table 31 shows that the most frequently used verbs in the health sciences tend to

co-occur more with collostructs. Interestingly, the verb suggest is collocated with that-

clause collostructs (76.80%). This result stresses the importance of the continuum of

lexis and grammar in theoretical and applied linguistics (Goldberg, 2006; Evans, 2009).

4.3.2. Collostructions in the Physical Sciences

As almost any verb is inclined to attract collostructs as well as content words,

naturally, verbs in the physical sciences did also render similar results. Table 31 shows

the most frequent five verbs each being observed to occur 68 times or over.

122

Table 32

Distribution of Most Frequent Verbs in the Physical Sciences

No Verbs Frequency %

1. find 241 7,53

2. show 144 4,50

3. provide 112 3,50

4. determine 68 2,12

5. indicate 68 2,12

Table 32 indicates that the first three verbs find, show and provide were used

over 100 times, while the two verbs determine and indicate were used over 50 times.

The results in Table 31 are in parallel with the results in BNC corpora (Biber, Conrad &

Reppen, 1999). Table 32 shows the most frequent verbs with their collostructs. As can

be seen from the table, the verb provide had no collostructs whatsoever, and only six in

the social sciences. Indeed, this verb does not even occur in the list of the most

frequently occurring verbs in the health sciences. Thus, that it has no collostructs here is

no surprise at all.

Table 33

Distribution of Collostructs of Most Frequent Verbs in the Physical Sciences

N Verbs Collostruct Frequency Percentage

1. Find That 51 20,90

2. Show That 59 40,97

3. Provide - 0 0

4. Determine whether/if 18 24,65

5. Indicate That 36 52,94

Table 33 shows that the verb indicate takes that-clause collostructs with the

highest percentage (52.94). The verbs show and find attracted that-clause collostructs at

40.97% and 20.90% respectively: and as indicated previously, the verb provide was not

used with any collostructs.

123

4.3.3. Collostructions in the Social Sciences

Each social context is supposed to produce both variations and similarities even

in particular contexts. The umbrella term in this study is only written academic prose

rather than other oral or written social contexts. Since the range of this study was based

on only written academic genre, the results showed some differences as well as

similarities. The most frequent five verbs in the social science are given in Table 33.

Table 34

Distribution of Most Frequent Verbs in the Social Sciences

Verbs Frequency %

find 230 3,19

understand 108 1.49

make 104 1,44

suggest 91 1,26

provide 89 1,23

Table 34 shows that the first three verbs find, understand and make were used

more than 100 instances, whereas the last two verbs suggest and provide were among

the most frequently used verbs with collocates. Table 35 shows the distribution of the

collostructs attracted to these verbs.

Table 35

Distribution of Collostructs of Most Frequent Verbs in the Social Sciences

Verbs Collostruct Frequency %

find that 106 15,89

make (See Table 34) 59 36.19

understand that 24 22,22

suggest that 74 81,31

provide somebody with something 6 6,89

Table 35 shows that the verb suggest attracted that-clause collostructs by

81.31%; understand, find and provide by 22.22%, 15.89% and 6.89% respectively. The

124

verb make in parallel with the results in BNC corpus (Biber et al., 1999) shows a wide

range of collostructs with 36.19%, and is given in Table 36.

Table 36

Distribution of the Collostruct of the Verb make in the Social Sciences

N Collostruct Frequency Percentage

1. make sth adjective 17 10,42

2. make sbd adjective 13 7,97

3. make sbd adjective 13 7,97

4. make sth do 7 4,29

5. make sbd do 6 3,68

6. make sth sth 6 3,68

7. make sbd sbd 3 1,84

8. make sbd done 2 1,22

9. make sth done 2 1,22

10. make sth into 2 1,22

11. make sbd sth 1 0,61

Table 36 shows that the verb make takes different collostructions in the social

sciences. Biber et al. (1999) indicate that the verb make is the most frequently used verb

among the first twelve verbs in BNC. These kinds of variations are not very often seen

in health and physical sciences. Hyland (2009) finds that each genre is dissimilar. In this

study, it was seen that the social sciences presented more various uses of verbs with

their collostructs.

4.4. Results Related to Research Question (3)

Research question 3: Is it possible to discover prototypical lexical collocations

according to the academic genre?

Since the 1970s it has been important to find prototypical concepts and

categories in psychology and linguistics studies. Inspired by the theory of

prototypicality (Rosch, 1977), this study intended to find an answer to this question. It is

important to pinpoint similarities as well as variations. The theory of prototypicality

125

(Hampton, 1979; Smith & Medin, 1981 strives to find some common properties

between concepts in order to understand how one conceptualizes concepts and the

world. The theory posits the idea that category membership can be defined by the best

example of a certain class. Categories and conceptual combinations are composed of

fuzzy properties, and cannot be determined by absolute defining properties. Rather, best

examples of a particular category produces resemblances, and form prototypes in mind.

Dictionary data are centered on the idea of prototypes, that is, the best examples of this

word, concept or category. The most frequent uses of particular verbs can presuppose

prototypicality of a certain verb+noun collocation. In this sense, it is quite critical to

extract some commonalities from this study and show prototypes in written academic

genre. The protoypicality was not hypothesized at all. The inclination towards this

question was neutral. Twelve identical verbs, out of the 30 most frequently used, were

chosen to show which verbs could be prototypes for each genre in this study. The

relationship and significance level between the genres were also investigated through

chi square analysis. Table 37 shows the most common verbs used across the three

genres.

Table 37

Chi-Square Analysis of Most Common Verbs across the Three Genres

Grup Total

Verbs Health Physical Social p p1-2 p1-3 p2-3

determine 110(9,0) 68(7,2) 51(5,1) 229(7,2)

0.0001

,000 ,000 ,000

Explain 39(3,2) 28(3,0) 68(6,8) 135(4,3) ,135 ,823 ,046

Find 311(25,4) 241(25,5) 230(22,9) 782(24,6) ,010 ,000 ,043

Identify 41(3,3) 22(2,3) 54(5,4) 117(3,7) ,455 ,005 ,000

improve 54(4,4) 37(3,9) 46(4,6) 137(4,3) ,015 ,000 ,317

Include 92 (7,5) 66 (7,0) 79 (7,9) 237 (7,5) ,729 ,010 ,040

Indicate 118(9,6) 68(7,2) 44(4,4) 230(7,2) ,803 ,003 ,010

Make 33(2,7) 56(5,9) 163(16,2) 252(7,9) ,046 ,490 ,090

provide 102(8,3) 112(11,8) 87(8,7) 301(9,5) ,000 ,000 ,000

represent 67(5,5) 55(5,8) 39(3,9) 161(5,1) ,130 ,002 ,000

show 133(10,9) 144(15,2) 89(8,9) 366(11,5) ,110 ,279 ,003

suggest 125(10,2) 49(5,2) 55(5,5) 229(7,2) ,012 ,048 ,000

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Table 37 shows that there was a significant difference across the three genres

(p<.0001). 50% of the twelve common verbs showed no significant difference between

the health and social sciences. However, 75% of the verbs showed a significant

difference between the health and social sciences, which can be interpreted that there

was more variation between these two sciences. Similarly, 66.66% of the verbs showed

a significant relationship between the physical and social sciences. The most frequently

used verbs can be said to have common collocative properties which are candidates of

prototypes in this study. The similar results are supported by Hyland and Tse’s (2005)

and Biber’s (1999) studies because the first five verbs in the former and the first twelve

verbs in the latter show a strong commonality. Therefore, it can be said that some

prototypes across the academic genres can be found at abstract level. The studies

starting with Rosch (1977) about prototypes were often carried out at one word level.

However, the following studies have striven to focus on conceptual combinations and

prototypes of these conceptual combinations (Murphy, 2004; Barsalou, 2005). These

results in this part may trigger studies in psychology at abstract level because this study

presented the verbs with their collostructs. Therefore, these results can help research not

only prototypes but also lexical priming (Hoey, 2005).

The next chapter will assess the study and results taking the research questions

and theoretical approaches into consideration. Besides, the implications and

recommendations for language learning and teaching will be presented with the

limitations of the study and suggestions for future research.

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CHAPTER 5

CONCLUSION

5.0. Introduction

This chapter addresses each research question and summarizes the findings

briefly. Subsequent to the summary and review of the research questions, the limitations

of the study are given. Lastly, some implications and recommendations for future

research are presented.

5.1. Summary of the Study

Understanding the nature of lexical collocations in its wider sense in corpus and

applied linguistics has given rise to various research questions. Some researchers even

theorized the collocations in linguistics, psychology and applied linguistics. Since the

foundation of collocations has not been thoroughly carried out, it is important to pose

new questions, and discuss theories and ideas. This study aimed to find answers to the

following research questions:

Research Question 1: What verb+noun lexical collocations (collostructions)

can be observed across academic genres?: The study showed that similar verbs were

used across the three academic genres: health, physical and social. However, these verbs

showed some variation in terms of the collocates they attracted. Collocates in the social

sciences showed more variation compared to those in the health and physical sciences.

The number of verbs taking collocates was more limited in the health and physical

sciences.

Research Question 2: How are these lexical collocations (collostructions)

constructed from a constructionist grammar view?: The study did not intend to deal

with collocations in a traditional sense. Rather, it included a question based on recent

research discussions and findings. The results of this study showed that the verbs in

written academic genres tended to occur with constructions besides only simply co-

occurring words. Almost each verb was seen to have its collostructional properties. It

was found that there were no pure verb+noun collocations in their pure and naïve form.

The most frequently used verbs with their collostructions showed a similar result in

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several studies as well (Thompson & Ye, 1991; Hyland, 1999, 2000; Hyland & Tse,

2005). In the three academic genres, one of the strongest collostruct was found to be

that-clause collostruct. This finding is important in that Goldberg (2006) stresses the

importance of the frequency and entrenchment of a specific construction.

Research Question 3: Is it possible to discover prototypical lexical collocations

(collostructions) according to the academic genre?: This last question intended to find

out whether the data could produce some prototypicalities similar to those in linguistics

and psychology. The results of the study showed that prototypes existed in the social

context of written academic prose. In general 165 common possible prototypical verbs

were detected, although statistically there seemed no significant relationship between

the genres. Out of these 165 common verbs, 12 most frequent and most common verbs

across the three genres were seen to have prototypical features at high frequencies. As

the degree of the frequency decreased, the variation of the verbs increased. This result is

also supported by Hyland and Tse (2007) stressing that only 8% and 10% of the words

show similar frequencies across different genres, and in terms of technical vocabulary,

only 5% of the running words indicate similarities implying that genres show

‘discursive variability’(p.251). It is not surprising that only a small percentage of the

data show similarities because each sub-discipline produces different combinations.

Therefore, Hyland and Tse (2007) approach academic vocabulary list with caution by

insistently stating that these kinds of results may refer to the misrepresentation of

academic literacy. Psychological explanation of conceptual combinations and linguistics

explanation of collocations have shown that it is a thorny issue to find prototypes at the

level of collocations (Murphy, 2004; Hyland & Tse, 2007). Hyland and Tse (2007) in

their study concludes that it may be pedagogically misleading for learners to direct them

to ‘overarching, universally appropriate teaching items’ (p.251).

5.2. Implications and Recommendations for Language Learning and Teaching

This study has revealed that similar verbs with their collocations across written

academic genres might be followed by advanced foreign and second language users so

that their academic writing and publication goals can be accomplished. Since each genre

requires certain conventions that each member of this genre is supposed to comply with,

learners are also expected to attend this community with full competence. Teachers

should help learners gain awareness of the fact that knowledge is socially constructed

129

within particular domains, and thus this line of thinking is reflected to academic writing

as well. This basic theoretical background in the minds of teachers can motivate

learners to pay attention to certain constructions in a certain genre.

More practically, learners need to be aware of not only common verbs used but

more importantly of the collocates each verb attracts because the main competence in

writing a professional article in a specific genre requires noticing certain collostructs in

this very particular discipline (Hoey, 2001, 2004; Hyland, 2008). Hyland (2008, p.561)

suggests that each learner should be trained in a ‘genre approach’ by teachers who are

supposed to regard texts as a dynamic ‘social interaction’ rather than only a sequence of

verbs given in a list. In parallel with this explanation, this study recommends teachers to

show the similarities and difference in using collocates. Teachers can direct their

attention to specific genres so that they can help learners notice lexico-grammatical

patterns in academic writing rather than present a list of verbs or nouns.

This study showed that teaching writing is beyond listing only similar content

words because each genre is specially and socially constructed and compromised

(Hyland, 2007). It is important for both teachers and learners to discover and develop

genre-specific corpora for themselves elaborately, and work on these constructions

together. Thus, Hyland (2007, p.251) stresses the fact that ‘discursive’ similarity as well

as variability should be noticed and detected by learners. In this sense, teacher educators

should introduce and guide teachers and learners into genre-oriented theory and

pedagogy presupposing that learners shall write only in socially constructed domains,

and learners should bear in mind that they are liberal and can be creative only within

constraints in order to attend the world of socially determined and constructed meanings

in academic writing because each genre refers to a particular social world with certain

patterns of language (Hoey, 2005; Hyland, 2007). The study has got significant

implications for English language learning and teaching, particularly specific to

academic writing in that while introducing academic texts to learners, teachers have a

reservoir of available data of collocations which they can put into the utilization of

language users while producing an academic text. This availability is bound to facilitate

the process of writing in general.

In terms of classroom application, teachers and learners have new roles in

language teaching and learning because they can constitute their own corpus in the

classroom so that they can extract their own collocations and reach reliable

generalizations over examples and exemplars. Before learners are asked to write about

130

an academic topic, as a warm-up activity forming a corpus in a two-three week period

might prepare learners to use the target language according to the specific topic or genre

they are supposed to write. Unless learners are entrenched and enriched by rich data of

corpus, deviant forms will be inescapable. Teachers should show learners how to

prepare an effective corpus instead of merely giving them hundreds of examples

through a concordancer.However, a concordancer can be used to check whether any

used collocation in a classroom setting is written or uttered by native speakers. Learners

should be able to revisit and recheck the data that they have extracted and studied.

Selective attention of learners may differ from each other in that each learner may

attend to different data. Therefore, learners can work together in order to share the data

they have chosen during the compilation and selection of lexical collocations (Lewis,

1998). This process will give learners the chance to negotiate the meaning of the data

together, which might reinforce learning. By doing so, teachers can give learners the

feeling that they are responsible for their own learning, and they learn to be independent

while learning a language.

Another implication for ELT is that material writers may have to review their

definition of lexical collocations because lexical collocations should also embrace

collostructions as well. Material writers should not treat lexis and grammar as separate.

Rather, they should show language learners that grammar and lexis can be learnt

concurrently (Lewis, 1998; Howarth, 1998). Material writers, in this sense, can help this

paradigm change in language learning settings take place. If material preparation

contains grammar-lexis activities, then learners will be able to perceive language as

holistic and integrative rather than dichotomic.

In terms of testing in ELT, testers should not measure grammar and lexis

separately. Rather, they should prepare exams that allow learners to reflect their

knowledge of collostructions as well. Since lexical collocations have syntactic functions

in language production, it is important to direct learners to focus on these

collostructions by developing certain tests containing both collocates and collostructions

instead of asking only the meaning of a certain word. It should be borne in mind that

each lexeme has its own intrinsic properties that should be perceived by learners.

Therefore, testers should gain an awareness of this new paradigm change in language

studies.

As a negative implication of this study, it can be said that language is constantly

changing, and the data they have collected may change over the years. In addition,

131

being obsessed with fixed expressions may lead learners not to use their creativity in

language. Foreign language learners might be able to use their creativity and make

contributions to the target language they learn. Therefore, coming up with creative

collocations by foreign or second language learners should not be regarded as

something negative. Rather, these creative collocations or collostructions should be

perceived as a contribution to the field. Instead of labeling these creative collocations as

errors, mistakes or deviances, it is better to treat them as possibly acceptable because

each new collocation is a candidate to be a part of language. In this sense, language

learners should be encouraged to make use of corpus data and to use their own

creativity.

5.3. Limitations of the Study

This study has been restricted to only verb+noun collocations with extended

meaning of collostruction in three written academic genres. The sampling is one of the

most discussed issues in corpus linguistics. Therefore, this study dealt with only a small

amount of corpus in a certain social context, academic prose in this study compared to

internationally recognized corpora such as BNC. Thus, the small scale of the corpus can

present only limited findings. Another limitation of the study was that the data were not

tagged or annotated. A fully elaborate comparative scrutiny with other academic

corpora was not carried out in spite of obtaining some results from these studies since

this was out of the scope of the study. In addition, only descriptive and chi-square

statistical tools were used in order to follow the expected associations.

5.4. Future Research

The main issue in corpus studies is that the data collected are increasingly

becoming larger and larger. Researchers are incessantly faced with a vast amount of

data. The main question as to what researchers can perform through such big data

remains quite critical and crucial. As this study dealt with limited amount of data, in the

future, by using a larger amount of data with more sophisticated and more easily

accessible software tools, some significant questions can be addressed in applied and

corpus linguistics. By comparing results of different corpora composed in various

research centers, more robust findings can be obtained. One of the main aims of corpora

132

studies has been to put intuition aside. However, the amount and comparison of larger

amount of data obtained through corpus studies can provide more reliability, validity

and objectivity. Verbs alone are rather complicated and intricate. The occurrence of

verbs with nouns and nominal phrases is more subtle since verbs are made up of distinct

and various layers, and range from single word to idiomatic units. In the future, several

steps can be followed : (i) carrying out detailed semantic and syntactic classification

and analysis of verbs and nouns (ii) extracting collostructs of verb+noun collocations,

(iii) conducting more sophisticated statistical analysis to measure the strength of

collocates and collostructs, (iv) constituting specialized corpora for particular academic

genres (v) integrating the findings in corpus linguistics with the studies in psychology

so that some links between conceptual combinations and colllocations can be

established, which may help researchers understand human mind at an abstract level,

(iv) being in cooperation with foreign language studies so that foreign and second

language users can make use of findings of obtained data, (vi) performing experimental

studies in foreign language settings after introduction of written academic genre corpora

is made, (vii) performing corpora studies to show that language can be understood on a

continuum of lexis and grammar, and last but not least, (viii) extending the definition

and meaning of collocation and explore new uses and functions of collocations through

corpora studies (ix) researching prototypes and lexical priming through the results of

these kinds of data-based studies.

133

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140

APPENDICES

List of Verbs across the Academic Genres

Appendix 1

List of Verbs in the Health Sciences

1. Abandon 2. Abolish 3. Abort 4. Abrogate 5. Absorb 6. Accelerate 7. Accentuate 8. Accept 9. Acclimate 10. Accommodate 11. Accomplish 12. Accumulate 13. Achieve 14. Acknowledge 15. Acquire 16. Activate 17. Add 18. Adjust 19. Administer 20. Admit 21. Adopt 22. Absorb 23. Advise 24. Advocate 25. Affect 26. Afflict 27. Afford 28. Aggravate 29. Align 30. Alkalinize 31. Alleviate 32. Allocate 33. Allow 34. Alter 35. Ameliorate 36. Amplify

37. Anaesthetize 38. Analyze 39. Anesthetize 40. Antagonize 41. Anticipate 42. Apply 43. Appose 44. Appreciate 45. Approve 46. Argue 47. Arouse 48. Arrange 49. Ascend 50. Ask 51. Assemble 52. Assert 53. Assess 54. Assist 55. Assume 56. Assure 57. Attach 58. Attain 59. Attempt 60. Attenuate 61. Attract 62. Attribute 63. Augment 64. Avert 65. Avoid 66. Avulse 67. Await 68. Become 69. Begin 70. Believe 71. Bend 72. Boil

73. Boost 74. Borrow 75. Breed 76. Bring 77. Broaden 78. Bruise 79. Build 80. Bury 81. Calculate 82. Calibrate 83. Capture 84. Carry 85. Catalyze 86. Catch 87. Categorize 88. Cause 89. Certifiy 90. Characterise 91. Choose 92. Circulate 93. Circumscribe 94. Cite 95. Clarify 96. Classify 97. Cleave 98. Administer 99. Collect 100. Colocalize 101. Colonize 102. Combine 103. Commence 104. Commit 105. Communicate 106. Compare 107. Compile 108. Complain

141

109. Complement 110. Complicate 111. Compose 112. Comprehend 113. Compress 114. Comprise 115. Compute 116. Concentrate 117. Conclude 118. Conduct 119. Confer 120. Confine 121. Confirm 122. Conform 123. Confront 124. Confuse 125. Connect 126. Conserve 127. Consider 128. Consolidate 129. Constitute 130. Constrain 131. Constrict 132. Consume 133. Contain 134. Contaminate 135. Continue 136. Contraindicate 137. Contribute 138. Convey 139. Convince 140. Coordinate 141. Corroborate 142. Corrolate 143. Counteract 144. Creat 145. Criticize 146. Critique 147. Crosslink 148. Cultivate 149. Curtail 150. Customize 151. Dampen 152. Daunt

153. Decant 154. Decapitate 155. Decease 156. Decerebrate 157. Decide 158. Declare 159. Decode 160. Deduce 161. Deem 162. Defer 163. Define 164. Deflesh 165. Deform 166. Degenerate 167. Dehydrate 168. Deionize 169. Delete 170. Delimit 171. Delineate 172. Deliver 173. Demonsrate 174. Denitrify 175. Denounce 176. Dentify 177. Deny 178. Deparaffinize 179. Depart 180. Depict 181. Deplete 182. Deploy 183. Depolarize 184. Depress 185. Deprive 186. Derange 187. Derive 188. Describe 189. Deserve 190. Designate 191. Destine 192. Destroy 193. Desynchronize 194. Detain 195. Detect 196. Determine

197. Detoriate 198. Detrain 199. Develop 200. Deviate 201. Devise 202. Devote 203. Diagnose 204. Dialyze 205. Dictate 206. Digest 207. Digitize 208. Dilate 209. Dilute 210. Diminish 211. Discard 212. Discern 213. Disclose 214. Discover 215. Discuss 216. Disfavor 217. Disinfect 218. Dislodge 219. Dismiss 220. Disperse 221. Displace 222. Display 223. Disqualify 224. Disrupt 225. Dissect 226. Disseminate 227. Dissolve 228. Distend 229. Distill 230. Distinguish 231. Distort 232. Distribute 233. Disturb 234. Diversify 235. Dominate 236. Donate 237. Downregulate 238. Drap 239. Drink 240. Drive

142

241. Edit 242. Educate 243. Eject 244. Elevate 245. Elicit 246. Eliminate 247. Elongate 248. Elucidate 249. Emphasize 250. Employ 251. Empower 252. Enable 253. Encode 254. Encompass 255. Encounter 256. Encourage 257. Enhance 258. Enjoy 259. Enlist 260. Enquire 261. Enrich 262. Enroll 263. Ensure 264. Entail 265. Enter 266. Envisage 267. Envision 268. Equalize 269. Equip 270. Eradicate 271. Establish 272. Evade 273. Evaluate 274. Evince 275. Evoke 276. Evolve 277. Exacerbate 278. Examine 279. Exceed 280. Excise 281. Excite 282. Exclude 283. Exctract 284. Execute

285. Exert 286. Exhibit 287. Expand 288. Expect 289. Explain 290. Explicate 291. Explore 292. Expose 293. Express 294. Extend 295. Extrapolate 296. Facilitate 297. Feed 298. Feel 299. Fight 300. Find 301. Finish 302. Fixate 303. Flatten 304. Float 305. Follow 306. Forget 307. Formulate 308. Foster 309. Found 310. Fractionate 311. Fulfill 312. Gain 313. Gather 314. Genarate 315. Generalize 316. Generate 317. Get 318. Give 319. Gnaw 320. Govern 321. Grasp 322. Guarantee 323. Halt 324. Halve 325. Handle 326. Hatch 327. Heighten 328. Highlight

329. Hinder 330. Hit 331. Hold 332. Homogenize 333. Hope 334. Hospitalize 335. Housekeep 336. Humidify 337. Hydrolyze 338. Hypothesize 339. Idealize 340. Identify 341. Ignite 342. Illustrate 343. Imagine 344. Immerse 345. Immobilize 346. Immunize 347. Impair 348. Implement 349. Implicate 350. Imply 351. Impoverish 352. Improve 353. Incite 354. Include 355. Incorporate 356. Incubate 357. Indebt 358. Indicate 359. Individualize 360. Induce 361. Induct 362. Infer 363. Inflate 364. Inflict 365. Inform 366. Infuse 367. Inhale 368. Inherit 369. Inhibit 370. Initiate 371. Inject 372. Inoculate

143

373. Instigate 374. Instruct 375. Integrate 376. Intend 377. Interpolate 378. Interrogate 379. Intersperse 380. Intrigue 381. Introduce 382. Invade 383. Invert 384. Investigate 385. Invite 386. Involve 387. Ionize 388. Irradiate 389. Isolate 390. Jeopardize 391. Join 392. Judge 393. Justify 394. Keep 395. Kill 396. Know 397. Lay 398. Learn 399. Leave 400. Lenghten 401. Lessen 402. Illustrate 403. Locate 404. Loosen 405. Lose 406. Maintain 407. Make 408. Manage 409. Manipulate 410. Marry 411. Match 412. Maximize 413. Mediate 414. Meet 415. Melt 416. Mention

417. Metabolize 418. Mimic 419. Minimise 420. Misclassify 421. Misdiagnose 422. Mitigate 423. Immerse 424. Immunize 425. Modify 426. Modulate 427. Motivate 428. Move 429. Impair 430. Implicate 431. Impose 432. ultigrave 433. Multiply 434. Mutate 435. Navigate 436. Necessitate 437. Necrotize 438. Neglect 439. Negotiate 440. Neutralize 441. Infiltrate 442. Nitrify 443. Normalize 444. Notarize 445. Notice 446. Inquiry 447. Install 448. Insulate 449. Integrate 450. Interact 451. Interest 452. Interface 453. Interleave 454. Interpret 455. Interrupt 456. Involve 457. Observe 458. Obtain 459. Obtrate 460. Obviate

461. Occlude 462. Occupy 463. Offer 464. Omit 465. Oppose 466. Optimize 467. Organize 468. Orient 469. Oscillate 470. Outgrow 471. Outnumber 472. Overcome 473. Overdiagnose 474. Overexpress 475. Overlook 476. Overpresent 477. Overshadowed 478. Oxidize 479. Palpate 480. Pasteurize 481. Pay 482. Perceive 483. Perforate 484. Perform 485. Perfuse 486. Permeabilize 487. Permit 488. Perpetuate 489. Perpolarize 490. Persuade 491. Pick 492. Pinpoint 493. Polymerize 494. Populate 495. Posit 496. Possess 497. Postpone 498. Postulate 499. Pour 500. Precede 501. Preclude 502. Predetermine 503. Predict 504. Preexist

144

505. Prefer 506. Preincubate 507. Prepare 508. Prescribe 509. Preserve 510. Presume 511. Pretreat 512. Prevail 513. Prevent 514. Procure 515. Produce 516. Prohibit 517. Prolong 518. Promise 519. Promote 520. Promulgate 521. Pronounce 522. Propagate 523. Propel 524. Propose 525. Protect 526. Protonate 527. Protrude 528. Prove 529. Provide 530. Provoke 531. Publish 532. Pull 533. Purchase 534. Purify 535. Pursue 536. Quote 537. Radiate 538. Raise 539. Randomise 540. Reach 541. Read 542. Realize 543. Rearrange 544. Receive 545. Recirculatee 546. Recognize 547. Recommend 548. Reconstitute

549. Reconstruct 550. Record 551. Recruit 552. Recrystallize 553. Recycle 554. Redefine 555. Redirect 556. Redistribute 557. Redo 558. Reduce 559. Reestablish 560. Reevaluate 561. Refine 562. Reflect 563. Refocus 564. Refold 565. Reformulate 566. Refuse 567. Regenerate 568. Regress 569. Regulate 570. Reinforce 571. Reinstitute 572. Reintroduce 573. Reject 574. Relieve 575. Remark 576. Remember 577. Remove 578. Render 579. Replace 580. Replenish 581. Represent 582. Repress 583. Require 584. Resemble 585. Resist 586. Resolve 587. Respire 588. Restore 589. Restrain 590. Restrict 591. Resume 592. Resuspend

593. Retain 594. Retest 595. Retrieve 596. Reveal 597. Revent 598. Revise 599. Revolutionize 600. Rewrite 601. Rinse 602. Roll 603. Rotate 604. Irrigate 605. Satisfy 606. Saturate 607. Say 608. Scavenge 609. Sear 610. Secrete 611. See 612. Seek 613. Select 614. Send 615. Sensitize 616. Shake 617. Share 618. Shed 619. Shiver 620. Shoot 621. Shorten 622. Show 623. Signify 624. Simulate 625. Situate 626. Skew 627. Smear¸ 628. Sniff 629. Soak 630. Solubilize 631. Solve 632. Sonicate 633. Soothe 634. Specialize 635. Specify 636. Speculate

145

637. Spend 638. Squeeze 639. Stab 640. Stabilize 641. Standardize 642. Stigmatize 643. Stimulate 644. Stipulate 645. Store 646. Stratify 647. Stretch 648. Strike 649. Subdivide 650. Submerge 651. Subscribe 652. Subside 653. Subtract 654. Suggest 655. Summarize 656. Superimpose 657. Supervise 658. Suppose 659. Suppress 660. Survive 661. Suspect 662. Sustain 663. Swim 664. Synchronize 665. Synergize 666. Synthesize 667. Tabulate

668. Tackle 669. Tailore 670. Take 671. Teach 672. Temper 673. Tempt 674. Tend 675. Terminate 676. Testify 677. Theorize 678. Think 679. Thread 680. Tie 681. Tolarate 682. Transcend 683. Transcribe 684. Transfect 685. Transform 686. Transmit 687. Transpose 688. Treat 689. Truncate 690. Try 691. Twist 692. Ultrasonicate 693. Undeflect 694. Underdiagnose 695. Underestimate 696. Undergo 697. Underlie 698. Underline

699. Undermine 700. Understand 701. Undertake 702. Underutilize 703. Underwrite 704. Unload 705. Upgrade 706. Upregulate 707. Utilize 708. Vaccinate 709. Validate 710. Ventilate 711. Verbalize 712. Verify 713. Visualise 714. Visualize 715. Volunteer 716. Want 717. Warrant 718. Weaken 719. Weigh 720. Withdraw 721. Withhold 722. Witness 723. Worsen 724. Write

146

Appendix 2

List of Verbs in the Physical Sciences

1. Abbreviate 2. Absorb 3. Accelerate 4. Accept 5. Accompany 6. Accommodate 7. Accompany 8. Accomplish 9. Accumulate 10. Achieve 11. Acknowledge 12. Acquire 13. Activate 14. Add 15. Adjoin 16. Adjust 17. Administer 18. Admit 19. Adopt 20. Advise 21. Advocate 22. Affect 23. Afford 24. Aggregate 25. Allocate 26. Alter 27. Ameliorate 28. Amplify 29. Analyse 30. Analyze 31. Anchor 32. Anesthetize 33. Anneal 34. Analyze 35. Answer 36. Anticipate 37. Apply 38. Appoint 39. Appreciate 40. Approve 41. Argue 42. Arrange 43. Articulate 44. Ascend 45. Ascertain 46. Assemble

47. Assert 48. Assess 49. Assign 50. Assist 51. Assume 52. Attain 53. Attenuate 54. Attract 55. Augment 56. Authenticate 57. Avoid 58. Become 59. Believe 60. Bend 61. Bind 62. Borrow 63. Bring 64. Broaden 65. Calculate 66. Calibrate 67. Capture 68. Carry 69. Catalyze 70. Categorize 71. Celebrate 72. Characterize 73. Circulate 74. Circumvent 75. Cite 76. Claim 77. Clarify 78. Collect 79. Colonize 80. Combine 81. Compare 82. Compete 83. Complicate 84. Compose 85. Compress 86. Comprise 87. Compute 88. Computeralize 89. Computerize 90. Conceal 91. Conceive 92. Conceptualize

93. Conclude 94. Condense 95. Conduct 96. Configure 97. Confine 98. Confirm 99. Confront 100. Confuce 101. Conjugate 102. Connect 103. Conserve 104. Consider 105. Constitute 106. Constrain 107. Construct 108. Consume 109. Contain 110. Continue 111. Contradict 112. Constrain 113. Convert 114. Convey 115. Convince 116. Cool 117. Cover 118. Create 119. Crystallize 120. Cultivate 121. Decompose 122. Decontaminate 123. Decorate 124. Decordicate 125. Decouple 126. Decrease 127. Decribe 128. Dedicate 129. Deduce 130. Defend 131. Defer 132. Define 133. Degenerate 134. Dehydrate 135. Delay 136. Delinquesce 137. Deliver 138. Demonstrate

147

139. Denote 140. Depict 141. Derivatize 142. Derive 143. Descend 144. Describe 145. Deserve 146. Desire 147. Destroy 148. Detect 149. Determine 150. Devastate 151. Develop 152. Devide 153. Digitize 154. Dilate 155. Dilute 156. Diminish 157. Disaggregate 158. Discard 159. Disconnect 160. Discover 161. Discretize 162. Discuss 163. Dislike 164. Dissolve 165. Disperse 166. Display 167. Disregard 168. Dissect 169. Dissolve 170. Distart 171. Distill 172. Distinguish 173. Distort 174. Distribute 175. Disrupt 176. Disturb 177. Diversify 178. Divide 179. Dominate 180. Downsize 181. Draw 182. Drive 183. Exclude 184. Edit 185. Elect 186. Electroplate 187. Elevate 188. Elicit

189. Eliminate 190. Elucidate 191. Embed 192. Embody 193. Embrace 194. Emphasize 195. Employ 196. Enable 197. Encapsulate 198. Encode 199. Encompass 200. Encourage 201. Endanger 202. Endorse 203. Endow 204. Enforce 205. Engineer 206. Engrave 207. Enhance 208. Enjoy 209. Enlarge 210. Ensure 211. Entail 212. Entangle 213. Enter 214. Entitle 215. Enumerate 216. Envisage 217. Envision 218. Establish 219. Estimate 220. Euthanize 221. Evaluate 222. Evaporate 223. Evoke 224. Examine 225. Excavate 226. Exceed 227. Excise 228. Exclude 229. Execute 230. Exercise 231. Exert 232. Exhaust 233. Exhibit 234. Expect 235. Expel 236. Explain 237. Exploit 238. Explore

239. Expose 240. Extend 241. Extract 242. Extrapolate 243. Fabricate 244. Facilitate 245. Fascinate 246. Feel 247. Find 248. Fluctuate 249. Follow 250. Formalize 251. Formulate 252. Found 253. Frustrate 254. Fulfill 255. Functionalize 256. Generalize 257. Generate 258. Get 259. rid 260. Give 261. Govern 262. Grip 263. Guarantee 264. Hamper 265. Handle 266. Highlight 267. Hinder 268. Identify 269. Illustrate 270. Implicate 271. Imply 272. Improve 273. Include 274. Indicate 275. Induce 276. Infect 277. Infer 278. Inhibit 279. Initiate 280. Inject 281. Interpolate 282. Introduce 283. Invest 284. Investigate 285. Invoke 286. Involve 287. Isolate 288. Idealize

148

289. İdentify 290. Ignore 291. Illustrate 292. Imagine 293. Imbed 294. Imitate 295. Immerse 296. Immobilize 297. Impact 298. Implement 299. Implicate 300. Imply 301. Impose 302. Improve 303. Incinerate 304. Incline 305. Include 306. Incorporate 307. Increase 308. Incubate 309. Incur 310. Indebt 311. Indicate 312. Induce 313. Industrialize 314. Infect 315. Infer 316. Infiltrate 317. Influence 318. Inform 319. Infuse 320. Inhibit 321. Initiate 322. Inject 323. Inoculate 324. Inspire 325. Install 326. Instruct 327. Insulate 328. Integrate 329. Intend 330. Intensify 331. Interconnect 332. Interpolate 333. Interpret 334. Intersect 335. Intersperse 336. Introduce 337. Invert 338. Invest

339. Investigate 340. Invoke 341. Involve 342. Join 343. Judge 344. Justify 345. Keep 346. Kill 347. Know 348. Learn 349. Leave 350. Lengthen 351. Let 352. Localize 353. Locate 354. Lose 355. Maintain 356. Make 357. Manage 358. Manipulate 359. Manufacture 360. Match 361. Maximize 362. Meet 363. Mention 364. Minimize 365. Modify 366. Modulate 367. Monitor 368. Motivate 369. Move 370. Mutate 371. Naturalize 372. Neglect 373. Neutralize 374. Normalise 375. Notice 376. Notify 377. Obey 378. Observe 379. Obtain 380. Occupy 381. Offend 382. Offer 383. Omit 384. Operate 385. Optimise 386. Optimize 387. Organize 388. Orient

389. Overcome 390. Overlie 391. Overproduce 392. Overrepresent 393. Overshadow 394. Oversimplify 395. Overturn 396. Overwhelm 397. Oxidize 398. Perceive 399. Perform 400. Permit 401. Populate 402. Pose 403. Possess 404. Predict 405. Prefer 406. Preform 407. Preheat 408. Predict 409. Prepare 410. Prescribe 411. Prevent 412. Produce 413. Prohibit 414. Promise 415. Promote 416. Propose 417. Protect 418. Protract 419. Prove 420. Provide 421. Publish 422. Pulverize 423. Purchase 424. Purge 425. Purify 426. Pursue 427. Quantify 428. Quantitate 429. Quench 430. Quote 431. Raise 432. Randomize 433. Rationalize 434. Reach 435. Read 436. Realize 437. Reanalyze 438. Rearrange

149

439. Reassign 440. Rebuild 441. Recall 442. Receive 443. Reclaim 444. Recognize 445. Recommend 446. Reconstitute 447. Reconstruct 448. Recover 449. Recurve 450. Redefine 451. Reduce 452. Refine 453. Reflect 454. Refluex 455. Reformulate 456. Refrigerate 457. register 458. Regulate 459. Reiase 460. Reinforce 461. Reject 462. Remember 463. Remind 464. Remodel 465. Remove 466. Render 467. Renew 468. Rent 469. Reorganize 470. Repeat 471. Rephrase 472. Replace 473. Replicate 474. Represent 475. Reproduce 476. Require 477. Resemble 478. Resist 479. Resolve 480. Restrain 481. Restrict 482. Resurrect 483. Retain 484. Retard 485. Retrieve 486. Reveal 487. Revise 488. Revisit

489. Revive 490. Rewrite 491. Run 492. Satisfy 493. Saturate 494. Say 495. Secrete 496. Secure 497. See 498. Seek 499. Select 500. Seem 501. Select 502. distribute 503. Settle 504. Shake 505. Shed 506. Shift 507. Shorten 508. Show 509. Signify 510. Simplify 511. Simulate 512. Situate 513. Solve 514. Specify 515. Speculate 516. Spend 517. Stimulate 518. Stop 519. Store 520. Straighten 521. Strengthen 522. Stretch 523. Strike 524. Submit 525. Substantiate 526. Subtract 527. Suggest 528. Summarise 529. Summarize 530. Sink 531. Superovulate 532. Supervise 533. Suppose 534. Suppress 535. Surround 536. Survive 537. Suspect 538. Sustain

539. Synthesize 540. Tabulate 541. Tackle 542. Take 543. Terminate 544. Testify 545. Thank 546. Thicken 547. Think 548. Threaten 549. Tolerate 550. Track 551. Train 552. Transmit 553. Treat 554. Trigger 555. Truncate 556. Try 557. Uncoat 558. Uncover 559. Underestimate 560. Undergo 561. Underline 562. Underrate 563. Underrepresent 564. Understand 565. Undertake 566. Unify 567. Update 568. Upturn 569. Utilize 570. Validate 571. Vegetate 572. Ventilate 573. Verify 574. Violate 575. Wander 576. Want 577. Warn 578. Warrant 579. Weaken 580. Wish 581. Wrap 582. Write

150

Appendix 3

List of Verbs in the Social Sciences

1. Abandon 2. Abduct 3. Abolish 4. Absorb 5. Accelerate 6. Accentuate 7. Accept 8. Accompany 9. Accomplish 10. Accumulate 11. Accuse 12. Achieve 13. Acknowledge 14. Acquaint 15. Acquire 16. Activate 17. Add 18. Address 19. Adjudicate 20. Adjust 21. Administer 22. Administrate 23. Admire 24. Admit 25. Admonish 26. Adopt 27. Adorn 28. Advance 29. Advise 30. Advocate 31. Affect 32. Affirm 33. Afford 34. Aggrieve 35. Agonize 36. Alienate 37. Allege 38. Allocate 39. Allow 40. Alter 41. Amalgamate 42. Amend 43. Amplify 44. Analyze 45. Annex

46. Annihilate 47. Announce 48. Answer 49. Antagonize 50. Anticipate 51. Apply 52. Appoint 53. Appreciate 54. Argue 55. Arouse 56. Arrange 57. Articulate 58. Ascertain 59. Ascribe 60. Ask 61. assail 62. Assemble 63. Assert 64. Assess 65. Assign 66. Assimilate 67. Assist 68. Assume 69. Assure 70. Astonish 71. Attain 72. Attempt 73. Attend 74. Attract 75. Attribute 76. Augment 77. Authenticate 78. Authorize 79. Avert 80. Avoid 81. Await 82. Awaken 83. Bear 84. Beat 85. Become 86. Befall 87. Begin 88. Beguile 89. Believe 90. Berate

91. betray 92. Blur 93. Boil 94. Bolster 95. Borrow 96. Bother 97. Break 98. Bring 99. broaden 100. Brutalize 101. Build 102. Burn 103. Bury 104. Buy 105. Calculate 106. Calibrate 107. Cancel 108. Capture 109. Cast 110. Catch 111. Categorize 112. Cause 113. Cease 114. Celebrate 115. Cement 116. Centralize 117. Certify 118. Challenge 119. Change 120. Characterise 121. Check 122. Choose 123. Circumscribe 124. Circumvent 125. Cite 126. Civilize 127. Claim 128. Clarify 129. Classify 130. Coerce 131. Collect 132. Colonize 133. Combat 134. Combine 135. Commence

151

136. Commend 137. Commit 138. Communicate 139. Compare 140. Compel 141. Compete 142. Compile 143. Complain 144. Complete 145. Complicate 146. Comply 147. Comport 148. Compose 149. Comprehend 150. Compress 151. Comprise 152. Compromise 153. Compute 154. Conceal 155. Concede 156. Conceive 157. Concentrate 158. Conceptualize 159. Conclude 160. Concur 161. Condemn 162. Condense 163. Conduct 164. Confabulate 165. Confer 166. Confess 167. Confide 168. Confine 169. Confirm 170. Conflate 171. Conform 172. Confound 173. Confront 174. Confuse 175. Connect 176. Conquer 177. Conserve 178. Consider 179. Consign 180. Consolidate 181. Conspire 182. Constitute 183. Constrain 184. Construct 185. Construe

186. Consult 187. Consume 188. Contain 189. Contect 190. Contemplate 191. Contend 192. Contextualize 193. Continue 194. Contradict 195. Contribute 196. Control 197. Convene 198. Convert 199. Convey 200. Convict 201. Convince 202. Cook 203. Coordinate 204. Cope 205. Correct 206. Correlate 207. Correspond 208. Corroborate 209. Counter 210. Cover 211. Create 212. Criticize 213. Cultivate 214. Curtail 215. Decentralize 216. Decide 217. Declare 218. Decode 219. Decompose 220. Deconstruct 221. Decorate 222. Decrease 223. Decriminalize 224. Dedicate 225. Deduce 226. Deepen 227. Defend 228. Define 229. Delegate 230. Delineate 231. Deliver 232. Demand 233. Demobilize 234. Demonstrate 235. Demystify

236. Denature 237. Denigrate 238. Denonunce 239. Denote 240. Deny 241. Depend 242. Depict 243. Deplete 244. Deploy 245. Depoliticize 246. Depress 247. derive 248. Describe 249. Deserve 250. Designate 251. Destine 252. Destroy 253. Detach 254. Detain 255. Detect 256. Deter 257. Determine 258. Devalue 259. Develop 260. Devise 261. Devote 262. Diagnose 263. Dictate 264. Differentiate 265. Diffuse 266. Digest 267. Dilute 268. Diminish 269. Disagree 270. Disapprove 271. Disarm 272. Discard 273. Discern 274. Disclose 275. Discourage 276. Discover 277. Discredit 278. Discuss 279. Disenfranchise 280. Disentangle 281. Dismantle 282. Dismiss 283. Disparage 284. Dispense 285. Disperse

152

286. Displace 287. Display 288. Displease 289. Dispose 290. Disqualify 291. Disrupt 292. Dissolve 293. Distance 294. Distil 295. Distinguish 296. Distort 297. Distract 298. Distribute 299. Disturb 300. Diversify 301. Divide 302. Dominate 303. Downgrade 304. Downplay 305. Draw 306. Drop 307. Earn 308. Eat 309. Educate 310. Elevate 311. Elicit 312. Eliminate 313. Embody 314. Embrace 315. Emphasize 316. Employ 317. Empower 318. Emulate 319. Enable 320. Enact 321. Enclose 322. Encourage 323. Endanger 324. Endorse 325. Endure 326. Enforce 327. Engage 328. Enhance 329. Enjoy 330. Enlarge 331. Enlighten 332. Enrich 333. Enroll 334. Enshrine 335. Enslave

336. Ensue 337. Ensure 338. Entail 339. Enter 340. Entertain 341. Enthrall 342. Entitle 343. Entrap 344. Entrench 345. Enumerate 346. Envisage 347. Envision 348. Escape 349. Eschew 350. Espouse 351. Establish 352. Estimate 353. Estrange 354. Evade 355. Evaluate 356. Evoke 357. Exacerbate 358. Examine 359. Excavate 360. Exceed 361. Excerpt 362. Exchance 363. Exclude 364. Exemplify 365. Exert 366. Exhaust 367. Exhibit 368. Exhort 369. Exlude 370. Exoticize 371. Expand 372. Expect 373. Expel 374. Explain 375. Explicate 376. Exploit 377. Explore 378. Expose 379. Expound 380. Express 381. Extend 382. Externalize 383. Extinguish 384. Extrapolate 385. Faciliate

386. Fail 387. Familiarize 388. Fantasize 389. Fascinate 390. Fight 391. Fill 392. Find 393. Finish 394. Fit 395. Follow 396. Forecast 397. Forego 398. Foreshadow 399. Forge 400. Forget 401. Forgive 402. Formulate 403. Foster 404. Found 405. Freeze 406. Frighten 407. Frustrate 408. Fulfill 409. Gain 410. Garner 411. Gather 412. Generalize 413. Generate 414. Get 415. Give 416. Glare 417. Govern 418. Grab 419. Grapple 420. Grasp 421. Gravitate 422. Graze 423. Greet 424. Guarantee 425. Guess 426. Guide 427. Hamper 428. Handle 429. Harass 430. Harm 431. Harmonize 432. Hassle 433. Hate 434. Haunt 435. Hear

153

436. Help 437. Hide 438. Highlight 439. Hinder 440. Hire 441. Hit 442. Hold 443. Hope 444. Humiliate 445. Hurt 446. Hypnotize 447. Idealize 448. Identify 449. Ideologize 450. Idolize 451. Ignite 452. Ignore 453. Illuminate 454. Illustrate 455. Imagine 456. Immigrate 457. Impair 458. Impare 459. Impart 460. Impend 461. Implant 462. Implement 463. Implicate 464. Imply 465. Impose 466. Impress 467. Improve 468. Impute 469. Incarcerate 470. Incite 471. Incline 472. Include 473. Incorporate 474. Increase 475. Indicate 476. Induce 477. Indulge 478. Infer 479. Infest 480. Infiltrate 481. Inflict 482. Influence 483. Inform 484. Inhibit 485. Initiate

486. Injure 487. Innovate 488. Inscribe 489. Insinuate 490. Insist 491. Inspect 492. Inspire 493. Instantiate 494. Instil 495. Institutionalize 496. Instruct 497. Integrate 498. Intend 499. Intensify 500. Interact 501. Interfere 502. Internalize 503. Interpenetrate 504. interpret 505. Interrelate 506. Interrogatee 507. Intervene 508. Intimidate 509. Intoxicate 510. Intrigue 511. Introduce 512. Invade 513. Invalidate 514. Invent 515. Invest 516. Investigate 517. Invite 518. Invoke 519. Involve 520. Jeopardize 521. Join 522. Justify 523. Juxtapose 524. Keep 525. Kick 526. Kill 527. Knit 528. Knock 529. Know 530. l 531. Lack 532. Lament 533. Launch 534. Lead 535. Learn

536. Leave 537. Legalize 538. Legitimize 539. Lend 540. Lessen 541. Let 542. Liberalize 543. Liberate 544. Limit 545. Loathe 546. Localize 547. Locate 548. Look 549. Loosen 550. Magnify 551. Maintain 552. Make 553. Manifest 554. Manipulate 555. Marginalize 556. Mark 557. Master 558. Match 559. Materialize 560. Maximize 561. Mean 562. Mediate 563. Meet 564. Mend 565. Mention 566. Minimize 567. Mislead 568. Misplace 569. Mispresent 570. Misread 571. Misrepresent 572. Mistreat 573. Misunderstand 574. Mitigate 575. Mobilize 576. Mock 577. Modernize 578. Modify 579. Monitor 580. Moralize 581. Motivate 582. Nationalize 583. Naturalize 584. Necessitate 585. Need

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786. Search 787. Seduce 788. see 789. Seek 790. Select 791. Sell 792. Send 793. Seperate 794. Serve 795. Settle 796. Sexualize 797. Shake 798. Share 799. Shed 800. Shift 801. Show 802. Signify 803. Situate 804. Sniff 805. Soften 806. Solve 807. speak 808. Specify 809. Speculate 810. Spend 811. Split 812. Stand 813. Stare 814. Start 815. State 816. Steal 817. Steer 818. Stem 819. Stifle 820. Stigmatize 821. Stimulate 822. stir 823. Stop 824. Strengthen 825. Stress 826. Strip 827. Study 828. Sublimate 829. Submit 830. Subordinate 831. Subscribe 832. Subsidize 833. Substitute 834. Subsume 835. Subvert

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CURRICULUM VITAE

KİŞİSEL BİLGİLER

Adı/ Soyadı : Eser ÖRDEM

Doğum Yeri : Adana

Doğum Tarihi : 13/03/1977

E posta adresi : eserordem@gmail.com

EĞİTİM DURUMU

Doktora : Çukurova Üniversitesi, İngiliz Dili Eğitimi (2013)

Lexical Collocations (verb + noun) across Written Academic

Genres in English

Yüksek Lisans : Muğla Üniversitesi, İngiliz Dili Eğitim (2005)

Retention and Use of Lexical Collocations (Verb+Noun and

Adjective + Noun) by Applying Lexical Approach in a Reading Course

Lisans : Çukurova Üniversitesi, İngiliz Dili Eğitimi

KİŞİSEL YETERLİLİKLER

Bildiği Yabancı Diller

İngilizce : TOEFL ( 108)

Almanca : 67.5 (ÜDS)

Çalıştığı Kurum ve Görevleri

2003-2004 : Ören İlköğretim Okulu

İngilizce Öğretmeni

2004-2005 : Muğla Üniversitesi

Araştırma Görevlisi

2005-2006 : Binghamton University, SUNY

Araştırma Görevlisi

2006- 2008 : Botaş International Limited

İngilizce Öğretmeni

2011- 2013 : Çukurova Üniversitesi

Yarı Zamanlı Öğretim Görevlisi

158

YAYINLAR , SUNUMLAR, KATILIMLAR

Dergi Yayınları

Ördem, Eser. (2013). Yabancılara Türkçe öğretiminde leksikal yaklaşım: bir

eşdizimlilik çalışması modeli, Adıyaman Üniversitesi Sosyal Bilimler Dergisi, 6

(11),905-931

Sunumlar

Ördem, Eser & M.Yüceol Özezen. (2007). Yüz İfadeleri Ve Dil Arasındaki İlişki:

Bilişsel Dilbilimsel Bir Yaklaşım, Türk Kültüründe Beden Sempozyomu,

Marmara Üniversitesi

Ördem, Eser. (2007). Türkiye Türkçesinde Zaman ve Vakit Metaforları : Bilişsel

Dilbilimsel Bir Yaklaşım, XXI. Ulusal Dilbilim Kurultayı, Mersin

Üniversitesi

Ördem, Eser. (2013). The Social Aspect of Language Acquisition : From Arabic

to Turkish. I. Uluslarası Türkiye’de Konuşulan Arapça Lehçeler ve Sözlü

Edebiyatları Sempozyumu, Mardin Artuku Üniversitesi1.Ulusla

Ördem, Eser. (2013). The Effect of First Language Surface Syntax on Second

Language Syntax. Uluslararası ELT Konferansı, Çukurova Üniversitesi

YURTDIŞI KATILIMLAR

2005-2006 : Fulbright Yabancı Dil Öğretimi Asistantlık Programı, NY,

USA