a new look at determiners in early grammar: phrasal...

A New Look at Determiners in Early Grammar:

Phrasal Quantifiers

Yu Kyoung Shin

(Sogang University)

Shin, Yu Kyoung (2012). A New Look at Determiners in Early Grammar: Phrasal Quantifiers. Language Research 48.3, 573-608.

This paper examines whether English-speaking children exhibit sys-tematic uses of determiners within phrasal quantifiers (PQs), which would be evidence for the determiner category in early grammars. Most previous studies investigate definite and indefinite articles in combination with nouns. However, the present study tests how children use all the determiners as part of PQs. Analysis of English native-speaker adult corpora reveals three groups of PQs based on following determiners: Phrasal quantifiers (1) obligatorily require a definite determiner (e.g., many of the toys); (2) are contextually de-pendent (e.g., a part of a/the toy); or (3) tend to precede a bare noun without a determiner (e.g., a number of toys). On the basis of these findings, this paper investigates data from English-speaking children aged one to five in the CHILDES corpus to see if they differ from adult data. Scrutiny of 2,502 tokens of 24 English PQs demon-strates that young children show patterns remarkably similar to adults’ patterns. Finally, individual variation is investigated by look-ing at the degree of overlap between a child and his father in how they employ determiners within PQs. In this child’s early produc-tions, the determiners are distributed much as in his father’s pro-ductions, providing further evidence that children’s usage is similar to that of adults.

Keywords: determiners, phrasal quantifiers, grammatical categories, language acquisition

1. Introduction

In this paper, I am interested in the specific English multiword phras-

es called phrasal quantifiers and in assessing whether very young English-

speaking children exhibit systematic uses of determiners as part of

phrasal quantifiers. There are two main theoretical accounts of the

574 Yu Kyoung Shin

syntactic category of determiner in early grammars. One is that young

children are innately equipped with the category of determiner (e.g.,

Valian 1986; Ihns & Leonard 1988; Valian, Solt, & Stewart 2009).

This is based on the assumption that the nature of children’s deter-

miner category undergoes no qualitative change during their develop-

ment. In the work of Valian (1986), two-year-old children have been

found to use determiners from the earliest stages of multiword speech

and to make remarkably few errors. Subsequent longitudinal studies

similarly found that young children have knowledge of the distribu-

tional properties of determiners with a variety of nouns (e.g., Ihns &

Leonard 1988).

Opponents of such nativist accounts have demonstrated that early

child does not have knowledge of determiner use and creates the

functional category of determiner at a later point. That is, early child

nominal structure lacks the determiner system but have lexical noun

phrases only (e.g., Radford 1990a, 1990b). According to Abu-Akel, Bailey,

and Thum (2004), children’s knowledge of early determiner use must

be gradual, because children’s omission errors in obligatory contexts,

starting from 18 months, gradually decrease until 36 months. Resear-

chers who support this view have reported findings that suggest that

early syntactic acquisition is based on limited scope formulae or rep-

ertoires of rote-learned phrases (e.g., Pine & Martindale 1996; Pine &

Lieven 1997; Tomasello 2000). Pine and Lieven (1997) pointed out

that a child with specific formulae such as where’s the X or that’s a X

would be expected to produce few determiner errors and they thus

proposed an “overlap test” to see whether children can use both of the

articles a and the before a noun in context. Pine and Lieven (1997)

analyzed the productions of 11 children, and they found little overlap

with respect to the noun types with which the definite and indefinite

articles were used, arguing against innate syntactic categories. Valian

et al. (2009) partially replicated the overlap test but included all of a

child’s determiners, not just a and the. The results, counter to Pine

and Lieven’s (1997), showed that children were able to use multiple

determiners before a noun to the same extent as their mothers. Despite

the existence of such previous studies, the research done in this area

thus far might not allow for an accurate description of children’s ear-

ly determiner use for at least three reasons.

First, the zero-article was excluded from the analyses. Many of the

A New Look at Determiners in Early Grammar: Phrasal Quantifiers 575

studies have concentrated on the and a/an (e.g., Pine & Martindale

1996; Pine & Lieven 1997; Abu-Akel et al. 2004) because these were

found by Valian (1986) to be the most frequently occurring deter-

miners in the speech of young children, together accounting for 72%

of all determiner tokens, and because these are considered the most

frequently occurring determiners in the English language (Sinclair 1991).

Master (1997), however, has noted that the zero article (Ø) is the

most frequent, followed by the, and then by a/an in five written gen-

res of works (i.e., research journals, science magazines, news magazines,

novels, and plays). He points out that a bare noun in the form of “Ø

+ noun” occurs with indefinite non-count nouns (e.g., there is lipstick

on his face) and plural count nouns (e.g., there are holes in your socks).

Although Valian et al. (2009) claimed to include all the children’s de-

terminers in their tests, they did not take the zero article into account.

In addition, there is a need to investigate determiners in contexts

where children’s limited formulae cannot be simply inserted. A large

number of studies (e.g., Valian 1986; Ihns & Leonard 1988; Pine &

Martindale 1996; Pine & Lieven 1997; Abu-Akel et al. 2004) have ex-

amined determiners in combination with nouns, which could be used

by children relying on the formulae of a specific determiner + noun

sequence. As a rule, the distribution of the determiners has been re-

stricted in the first place by the noun. It has been argued, for in-

stance, that a determiner (D) is a grammatical element that combines

with a Noun Phrase (NP) complement (Abney 1987; Abu-Akel et al.

2004). This structure constitutes a Determiner Phrase (DP) and the

functional category D is the head of DP with a nominal phrase.

Figure 1 shows the structure of a DP in English where D can be def-

inite and indefinite determiners.

However, determiners do not always behave in this manner. To

take a determiner embedded in particular kinds of phrases as an ex-

ample, the use of the determiner might differ according to the preced-

ing phrase. Phrasal quantifiers, typically consisting of a quantifier (or a

quantificational noun) with an of-PP, modify an embedded noun to

express a partial quantity or number out of a totality (e.g., A Compre-

hensive Grammar of the English Language, 1985; Cambridge Grammar of

English, 2006). As in example (1), the embedded determiner within a

phrasal quantifier must be definite.

576 Yu Kyoung Shin

Figure 1. The D-system (Abu-Akel et al. 2004: 409).

(1) Many of the toys are sold out.

Many of the toys would be derived from an underlying configuration

of (1’) below. The Quantifier Phrase (QP) combines with DP taking

of-PP as a complement. The quantifier (e.g., many) is interpreted ana-

phorically, that is, referring to a subset expressed by the embedded

noun in the of-PP, and therefore, it requires the embedded determiner

to be definite (e.g., the toys, his toys, these toys).

(1’) The structure of QP

On the other hand, another phrasal quantifier, a number of, which

shares the semantic properties of many of, does not always take defi-

nite determiners, as in the examples in (2).

DP

~ Spec D'

~ D NP

I I (in)definite N'

I N

QP ~

Q pp

I~ many P DP

I I of 0'

~ D NP

I I DEFINITE N'

I I the N

I toys


(2) a. A number of the toys are sold out.

b. A number of toys are sold out.

The underlying structure of a number of (the) toys will have the struc-

ture of (2’). The structure of (2’) differs from (1’) in that the quantifi-

cational noun (i.e., number) behaves like an ordinary count noun. The

quantificational noun serves as a head with an of-PP complement,

and it allows either definite or indefinite embedded determiners in

context. The definite determiners (e.g., definite article the, demonstra-

tives, and possessives) refer to a group contextually indicated, whereas

the indefinite determiners (e.g., indefinite article a/an and the zero ar-

ticle) have the function of introducing an as of yet undefined group.

(2’) The structure of NP

Opponents of nativist syntactic accounts have noted that children’s

few determiner errors are derived from the noun slot of determiner +

noun sequences. However, the determiners within phrasal quantifiers

seem to be a matter of the preceding phrase: some phrasal quantifiers

obligatorily require definite determiners (e.g., many of the toys), while

others optionally take definite determiners in context (e.g., a number of

(the) toys). Therefore, determiner use cannot be accounted for by a

specific determiner + noun sequence, at least in phrasal quantifiers. If

children fail to use determiners properly according to the specific

phrasal quantifiers, this would suggest that young children may not

NP

---\ o N'

I.~ a N pp

I~ number P DP

I I of D'

~ o NP

I I (IN )DEFINITE N'

I I (the) N

I

toys

578 Yu Kyoung Shin

have knowledge of the category of determiner. If, alternatively, the

children use relevant determiners as part of phrasal quantifiers, this

would argue in favor of them having adult-like knowledge of the cat-

egory of determiner. On this note, investigating determiners within

phrasal quantifiers should provide evidence for either the syntactic or

limited scope account of children’s early determiner use.

The third reason that more research is needed on this topic is that

the number of tokens in the previous studies has not been large enough

to convincingly detect patterns with determiners in the speech of

young children. A paucity of opportunities for the participants to pro-

duce the forms of interest could create data that would mislead re-

searchers into favoring one of the two accounts. For example, assume

a child produces many of the toys, many of Yukyoung’s toys, many of

these toys, and many of those toys. Assume also that another child uses

only many of the toys several times. The latter case could be seen as a

specific formula, for instance many of the or the toys, even if the child

did possess a syntactic category of determiner.

The specific goals of this paper are twofold. The first is to examine

all of the relevant data from English-speaking children aged one to

five in the CHILDES corpus in order to see if they differ from adult

data. The second goal is to analyze the data from one child who was

observed in a longitudinal study to capture individual variation. I first

investigate how adults use phrasal quantifiers with respect to follow-

ing determiners in two English native speaker corpora in the first

study (Section 2). Next, I compare these findings with the children’s

data from CHILDES in the second study (Section 3). Then, I inves-

tigate the degree of overlap in the phrasal quantifiers in which a child

and his father use different determiner types in the third study (Section

4).

2. Adults’ Determiner Use Within PQs from Native Corpora

2.1. Method

The first study draws data from two English native speaker adult

corpora. The Brown University Standard Corpus of Present-day Ameri-

can English (henceforth Brown) is the first modern corpus of the


English language, which was collected in the early 1960s. It was com-

piled using 500 chunks of approximately 2,000 words of written texts,

resulting in 1,014,300 words (Kennedy 1998). Another synchronic cor-

pus, comparatively used with Brown, is the Freiburg-Brown Corpus of

American English (henceforth Frown). It contains about one million

words of written American English as used in 1991 (McEnery, Xiao,

& Tono 2006). In this study, using the Brown and the Frown, 43

English phrasal quantifiers were identified, and over 6,000 contextua-

lized tokens that occur in the two corpora were closely examined and

categorized in terms of following determiners.

2.2. Results

The Brown corpus and the Frown corpus yielded 6,042 occurrences

of 43 English phrasal quantifiers (see Appendix 1 for details). As

summarized in Table 1 below, the English phrasal quantifiers do not

all behave in the same way regarding a following determiner, but seem

to fall into three groups: (1) some phrasal quantifiers obligatorily re-

quire a definite determiner (98.9%); (2) others are quite context-de-

pendent and optionally take a determiner; (3) the rest mostly precede

a bare noun without a determiner (92.3%). For convenience, I shall

hereafter refer to these three groups as Det(erminer)-obligatory, Con-

textual, and Zero-likely, respectively.

Table 1. The number of tokens of following words after phrasal quantifiers

Definite determiners

a/an Ø Totalthe

demon-

strative

posses-

sive

definite

pron.other1) Total

Det-

obligatory

1.606

(46.8%)

305

(8.9%)

613

(17.9%)

450

(13.2%)

418

(12.1%)

3,392

(98.9%)

312)

(0.9%)

7

(0.2%)

3,430

(57%)

Contextual527

(41.3%)

75

(5.9%)

162

(12.7%)

64

(5%)

150

(11.7%)

978

(76.6%)

135

(10.6%)

164

(12.8%)

1,277

(21%)

Zero-likely37

(2.7%)

15

(1.1%)

13

(1%)

20

(1.5%)

16

(1.2%)

101

(7.5%)

2

(0%)

1,232

(92.3%)

1,335

(22%)

Total2,179

(36%)

395

(6%)

788

(13%)

534

(9%)

584

(10%)

4,471

(74%)

168

(3%)

1,403

(23%)

6,042

(100%)

Note: Ø refers to zero article.

580 Yu Kyoung Shin

Table 2 lists the phrasal quantifiers that belong to each group. There

are 19 phrasal quantifiers in Det-obligatory, 12 in Contextual, and 12

in Zero-likely. Although the phrasal quantifiers in the group of Zero-

likely tend to be followed by bare nouns, they, in principle, belong to

the Contextual group in that Zero-likely group share the same syntac-

tic structure with Contextual group (see 2’). In both groups, they al-

low either definite or indefinite embedded determiners in context, de-

spite different frequencies of following determiner use: the Contextual

group takes the zero article (Ø) 12.8% of the time, and the Zero-like-

ly group, 92.3% of the time.

Table 2. A list of phrasal quantifiers in three groups

Phrasal quantifiers

Det-obligatory

any of, none of, one of, each of, either of, (a) few of, (a) little of, an-

other of, certain of, some of, several of, half of, both of, many of, most

of, all of, the rest of, the remainder of, the whole of

Contextualenough of, much of, more of, less of, majority of, minority of, mass of,

part of, portion of, remnant of, section of, segment of

Zero-likely

a couple of, a lot of, lots of, loads of, plenty of, tons of, a heap of, an

abundance of, an amount of, a good/great deal of, quantity of, num-

ber of

In the second study, I investigate the young children’s data in CHILDES

to see whether the children display the same pattern of determiner

use within phrasal quantifiers as adults.

3. Young Learners’ Determiner Use within PQs from CHILDES

3.1. Method

The data for the second study came from transcripts of the sponta-

neous speech of English-speaking children aged from 1;4 (e.g., can I

have some of that? in MacWhinney 2000) to 5;11 (e.g., they keep part of

1) Other includes proper nouns, wh-words, reflexive pronouns, quantifications, and numbers.

2) Of the 0.9% (31 tokens), most of the tokens involve indefinite articles in idiomatic phrases (e.g., all of a sudden).


it folded, in Gathercole 1980), obtained through the Child Language

Data Exchange System (CHILDES, MacWhinney 2000). This study

includes every occurrence of a phrasal quantifier in the entire corpus

(see Appendix 2 for details for subjects). Out of 43 phrasal quantifiers,

a total of 2,502 tokens of 24 phrasal quantifiers occurred: 15 phrasal

quantifiers in the Det-obligatory group (any of, none of, one of, each of,

either of, (a) few of, (a) little of, another of, some of, half of, both of, many

of, most of, all of, the rest of ), 4 in the Contextual group (enough of,

much of, more of, part of ), and 5 in the Zero-likely group (a couple of,

a lot of, lots of, plenty of, tons of ). All the tokens were classified ac-

cording to determiner types into five age groups (see Appendices 3 to

8 for details) and errors in each age group were scrutinized.

All possible examples of errors were coded as (1) misuse of Det (a)

wrong use (e.g., some of that toys), (b) Det without a noun (e.g., some

of your.); (2) underuse of Det (e.g., most of lizard ); (3) overuse of Det

(e.g., all of these that stick); (4) Others (a) formation (e.g., one of mines),

(b) plurality (e.g., lots of moneys). Children’s immediate repetitions of

adults’ phrasal quantifiers were excluded from the data (e.g., Adult:

that’s part of the top � Child (1;9): that part of the top, in Brown 1973),

and unclear utterances (e.g., all of the xxx) were also removed.

3.2. Results

Tables 3, 4, and 5 display percentages and the number of tokens of

determiners within phrasal quantifiers. As seen here, children show

patterns remarkably similar to adults’ patterns with all three deter-

miner groups. With the Det-obligatory group, adults used definite de-

terminers 98.9% of the time, and children, 96.8% of the time (Table

3; see also Appendix 9 for details for each age group). With the Con-

textual group, both children and adults use definite determiners most

frequently, and have a much lower usage of the zero article, followed

by a/an (Table 4; see also Appendix 10). In Zero-likely, children and

adults demonstrated almost the same pattern of determiner use: both

groups used the zero article at a rate of about 92% and definite deter-

miners 7.5% of the time (Table 5; see also Appendix 11).

582 Yu Kyoung Shin

Table 3. Det(erminer)-obligatory.


a/an Ø Totalthe

demon-strative

posses-sive

definite pron.

other Total

Children303

(19.5%)268

(17.3%)231

(14.9%)636

(41%)63

(4.1%)1,501

(96.8%)14

(0.9%)36

(2.3%)1,551

(100%)

Adults1,606

(46.8%)305

(8.9%)613

(17.9%)450

(13.2%)418

(12.1%)3,392

(98.9%)31

(0.9%)7

(0.2%)3,430

(100%)

Table 4. Contextual.


a/an Ø Totalthe

demon-strative

posses-sive

definite pron.

other Total

Children76

(35.5%)18

(8.4%)38

(17.8%)34

(15.9%)7

(3.3%)173

(80.9%)18

(8.4%)23

(10.7%) 214

(100%)

Adults527

(41.3%)75

(5.9%)162

(12.7%)64

(5%)150

(11.7%)978

(76.6%)135

(10.6%)164

(12.8%)1,277

(100%)

Table 5. Zero-likely.


a/an Ø Totalthe

demon-strative

posses-sive

definite pron.

other Total

Children5

(0.7%)3

(0.4%)4

(0.5%)36

(4.9%)7

(1%)55

(7.5%)0

(0%)682

(92.5%) 737

(100%)

Adults37

(2.7%)15

(1.1%)13

(1%)20

(1.5%)16

(1.2%)101

(7.5%)2

(0.2%)1,232

(92.3%)1,335

(100%)

Overall, children made few errors with determiners as part of phrasal

quantifiers. Within 2,502 phrasal quantifier tokens, only 88 deter-

miners were flagged as possible errors, occurring at rates ranging from

2.2% in the age 4 group to 7.1% in the age 1 group. A summary of

all possible errors is presented in Table 6.


Table 6. Summary of possible determiner errors as part of phrasal quantifiers

Error Type Age 1 Age 2 Age 3 Age 4 Age 5

MISUSE

1) Wrong use

(e.g., some of that toys)0(0%) 3(0.6%) 4(0.5%) 5(0.5%) 2(0.6%)

2) Det without a noun

(e.g., some of your.)0(0%) 1(0.2%) 3(0.4%) 1(0.1%) 0(0%)

UNDERUSE

1) Omission of Det

(e.g., most of lizard)0(0%) 6(1.3%) 7(0.9%) 5(0.5%) 2(0.6%)

OVERUSE

1) Sequenced Dets

(e.g., all of these that stick)0(0%) 0(0%) 0(0%) 1(0.1%) 3(1%)

OTHERS

1) Formation

(e.g., one of mines)0(0%) 5(1.1%) 7(0.9%) 3(0.3%) 2(0.6%)

2) Plurality

(e.g., lots of moneys)1(7.1%) 8(1.7%) 10(1.2%) 5(0.6%) 4(1.3%)

Error/Total token

(Error rate %)

1/14

(7.1%)

23/469

(4.9%)

31/807

(3.8%)

20/903

(2.2%)

13/309

(4.2%)

Of the possible errors, three error types (i.e., misuse, underuse, and

overuse of Det) were considered “actual errors” that are directly re-

lated to the determiner use. There are a total of 43 actual errors: 19

where the determiner was misused – either wrong use of a determiner

(e.g., age 2, one of that toys sing; age 3, part of Paul chair; age 4, lots of

messy a, down on the ground; age 5, some of a high number) or a deter-

miner without a noun (e.g., age 2, they have lots of real; age 3, I can

find some of your; age 4, some of the on the bottom huh?), 20 where the

determiner was underused – omission of a determiner before a noun

(e.g., age 2, one of kids don’t want the Daddy; age 3, where do the rest of

pieces pieces go?; age 4, that’s a part of train; age 5, there are too many of

tooth brushed), and four where the determiner was overused – se-

quenced determiners (e.g., age 4, there’s a lot of these this kind; age 5,

I could put all of these that stick go like that).

The rest of the errors were excluded from the actual errors – 17 in-

584 Yu Kyoung Shin

volving formation errors (e.g., age 2, part of my squeezes; age 3, I got a

lot of powerfuls; age 4, one of em’s mines?; age 5, all of them pills) and

28 involving plurality errors (e.g., age 1, one of that [polar bears], age

2, lots of boot; age 3, a lot of person; age 4, tons of the cat; age 5, lots

of parking garage). These errors do not seem to be directly relevant to

determiner usage. For example, children were aware of the proper

types of determiners (i.e., definite or indefinite) according to phrasal

quantifiers, even when the formation of the determiners was incorrect.

And the plurality errors might suggest that they do not have full

knowledge of the singular/plural noun distinction. Neither error type,

however, has any connection with knowledge of the distributional

properties of determiners within phrasal quantifiers.

Table 7 shows the actual determiner errors overall. The average

number of errors for all age groups is 8.6 tokens (1.7%). There was

no significant difference between ages, confirming previous findings

that children make few determiner errors at any age (e.g., Valian

1986; Ihns & Leonard 1988; Valian et al. 2009).

Table 7. Actual determiner errors by age.

Age group Actual error (%) Total

1 0 (0%) 14

2 10 (2.1%) 469

3 14 (1.7%) 807

4 12 (1.3%) 903

5 7 (2.3%) 309

Total 43 (1.7%) 2,502

As seen in Table 7, none of the children below age 2 made errors in-

volving determiner use (other than formation or plurality errors). When

children started to use determiners with phrasal quantifiers, they used

them correctly, as in (3) to (7).

(3) can I have some of that? (Ross 1;4, in MacWhinney 2000)

(4) that car doesn’t fit the train (.) get the rest of the train (Peter

1;9, in Bloom 1970)

(5) all of the toys. (Child 1;9, in Valian 1991)


(6) plenty of sea seaweed (Ethan 1;10, in Demuth, Culbertson, &

Alter 2006)

(7) there are lots of clothes (Lily 1;11, in Demuth, Culbertson, &

Alter 2006)

Children’s correct use cannot be accounted for by reliance on for-

mulae of determiner + noun sequences, counter to the claims of Pine

and Martindale (1996) and Pine and Lieven (1997). Lexically fixed

formulae cannot explain how children use multiple determiners within

one phrasal quantifier as in (8), and how they use the same noun

with different determiners according to the phrasal quantifiers as in

(9). All of the examples are from one child, Abe (3;1), in Kuczaj (1977).

(8) a. did I eat all of my dinner?

b. all of these little cars and look at this look at this (.) Mom

c. one love you I could get all of the milk

(9) a. a loud noise came and all of the animals were watching and

watching and hiding zoom!

b. yeah it might be Greggy’s (be)cause Greggy got lots and lots

of animals

In particular, determiner usage in the Contextual group provides the

most conclusive evidence for an innate syntactic category of determi-

ner. Some might argue that when children frequently use a specific

phrasal quantifier with a specific determiner (e.g., all of the, lots of ),

they could be producing rote-learned phrases and, for this reason

alone, making few errors. However, this argument is irrelevant in re-

gard to the phrasal quantifiers that do not occur only with particular

determiners. Children’s ability to produce phrasal quantifiers in the

Contextual group rules out the possibility that children are limited to

using lexically specific formulae. As shown in (10), part of takes mul-

tiple determiners according to context. All the following examples are

from Naima (2;10) in Demuth, Culbertson, and Alter (2006).

(10) a. it needs to be some part of this

b. is this part of an animal?

c. this part of it has to go somewhere

586 Yu Kyoung Shin

d. well you know the chair is all clean, this is part of the chair

To summarize, the second study demonstrates that, based on aver-

aged data, children and adults show very similar patterns with deter-

miners as part of phrasal quantifiers. Furthermore, children in each

age group made few determiner errors. These results argue against

findings that suggest that children’s early determiner use is much

more limited in scope than that of adults.

4. A Longitudinal Study of a Single Child

4.1. Method

The third study utilizes data from Ross (1;4 to 5;11), a participant

in MacWhinney (2000), obtained through CHILDES. In a partial rep-

lication of the work of Valian et al. (2009), I examined the overlap of

all determiners within phrasal quantifiers, including the zero article,

to find out how much the child was able to vary determiners within

any phrasal quantifier. Demonstratives without a following noun were

not counted because this, that, these, and those appear to function as

NPs rather than as determiners (Valian 1986). For example, the first

occurrence of a determiner recorded in the child’s speech (1;4, can I

have some of that?) was excluded because that refers to Det + noun.

Thus, the next occurrence (2;6, I gotta do a lot of poo) was counted as

his first phrasal quantifier for this analysis.

All utterances that contained phrasal quantifiers were coded. To de-

termine the degree of determiner overlap in the phrasal quantifiers of

both the child and his father, I counted (1) the number of phrasal

quantifiers, (2) the number of determiner types within phrasal quanti-

fiers, (3) how often each noun appeared with more than one deter-

miner type within any phrasal quantifier, and (4) how often each

noun appeared with any determiner type within any phrasal quanti-

fier. To calculate the amount of overlap for the child and his father,

the numerator was the number of noun types that occurred with more

than one determiner type within a phrasal quantifier (and/or with dif-

ferent phrasal quantifier types); the denominator was the number of

noun types that occurred at least once with any determiner type with-


in any phrasal quantifier. For example, Ross, at age 3, used four

nouns with different phrasal quantifiers (i.e., a lot of water, the rest of

the water / a couple of days, a lot of days / a lot of people, all of the people

/ a lot of love, lots of love). And there were a total of 37 words that

Ross used at least once with any determiner type within any phrasal

quantifier. That is, the degree of overlap for Ross at the age of 3 was

calculated as 100 × 4/37 = 10.8%.

4.2. Results

Table 8 shows overlap percentages for all determiners within phras-

al quantifiers for the child and his father. There was no significant

difference between the amount of overlap shown by the child (on

average, 12.9%), and the amount shown by his father (on average,

12.4%). This suggests that the child used different determiners within

phrasal quantifiers almost as often as his father; this argues against

the claims of Pine and Martindale (1996). If a lexically specific ac-

count of children’s early determiner use was correct, the child would

have shown less overlap than his father.

Table 8. Determiner overlap within phrasal quantifiers

By age Dets overlapPhrasal Quantifier

(PQ) types

Det types

within PQs

Child

2 10% (1/10) 2 1

3 10.8% (4/37) 13 8

4 16% (4/25) 8 8

5 13.8% (4/29) 11 8

Total 12.9% (13/101) 19 11

Father

2 12.1% (4/33) 8 9

3 9.7% (6/62) 12 13

4 11.8% (4/34) 11 9

5 17.1% (7/41) 12 7

Total 12.4% (21/170) 18 14

In (11) to (14) appear examples of how Ross varied his determiners

588 Yu Kyoung Shin

from age 2 to age 5.

(11) a. no more lots of noise?

b. I mean skate boards don’t make a lot of noise

(12) a. the rest of the water went on the scarecrow

b. there was a lot of water to keep those apples up.

(13) a. one of the actions figures broke at preschool

b. we’ve got a lot of action figures to buy

(14) a. I did one of the little things (.)

b. one of those things you have_to do [//]

c. lots of things.

Furthermore, Ross showed no significant difference in overlap at dif-

ferent ages. As his age increased, however, he used more determiner

types. For instance, he used only one type of determiner, the zero ar-

ticle, at age 2 and started to use eight types from age 3. But this

does not seem to be due to development in the nature of children’s

determiners. Although at age 2, he only produced phrasal quantifiers

with the zero article, this is understandable considering the fact that

he only used phrasal quantifiers in the Zero-likely group (e.g., a lot

of, lots of ). From age 3, the child started to use some phrasal quanti-

fiers in all three groups (e.g., one of, little of, another of, some of, all of,

rest of, more of, part of, a couple of, a lot of, lots of, plenty of, tons of ),

exhibiting the same number of determiner types at ages 4 and 5. As

shown in Valian et al.’s (2009) study, development will be limited to

adding knowledge of the particulars of each determiner’s behavior to

the abstract determiner category, which is already well-articulated.

In sum, the results of the third study showed no difference between

the child and his father in their overlap of determiners within phrasal

quantifiers. The child’s patterns were very similar to his father’s, and

there was also no qualitative change of development in the child’s us-

age with age. These findings suggest that the production of both the

child and his father is based on an underlying, innate syntactic cat-

egory of determiner.


5. Conclusion

The primary purpose of this paper was to test if very young chil-

dren have knowledge of a syntactic category of determiner by inves-

tigating how they employ determiners within phrasal quantifiers. The

results demonstrate that they use multiple determiners as part of

phrasal quantifiers to the same extent as adults and with very few er-

rors at any age.

First, I examined two native adult corpora, and I found that English

phrasal quantifiers can be put into three groups according to follow-

ing determiners: Det-obligatory, Contextual, and Zero-likely (see Tables

1 and 2). With these findings as a basis, I next measured the fre-

quency of all phrasal quantifiers that appear in all the data from

English-speaking children aged one to five in the CHILDES corpus.

Children show patterns of determiner usage within phrasal quantifiers

that are remarkably similar to the patterns of adults. Furthermore, the

percentage of errors (on average, 1.7%) in the children’s production is

very small, and there is no significant differences between the differ-

ent age groups. From the very beginning of their phrasal quantifier

use, children manipulate determiners correctly. This ability cannot be

due to children’s reliance on rote-learned phrases, because they are

able to vary determiners within a single phrasal quantifier (e.g., many

of the toys and many of my toys) and employ the determiners proper to

specific phrasal quantifiers (e.g., many of the toys and a lot of toys),

which cannot be accounted for by knowledge of specific determiner +

noun sequences (e.g., the toys).

Next, I examined the amount of overlap for one child and his fa-

ther – that is, how many times the child and his father used each

noun with different determiner types within phrasal quantifiers. In

this overlap test, there was no significant difference between the child

and his father, with each having an average of about 12%. This result

also confirms that in children’s early production, determiners are dis-

tributed across nouns within phrasal quantifiers to the same extent as

adults. Taking the three studies together, this paper provides positive

evidence for a syntactic category of determiner in the speech of young

children.

590 Yu Kyoung Shin

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Appendix 1. Tokens and percentage of determiners within phrasal

quantifiers in the Brown corpus and the Frown corpus

thedemon-strative

posses-sive

definite pron.

other a/an Ø Total

any of 58(42%) 17(12%) 21(15%) 19(14%) 21(15%) 3(2%) 0(0%) 139

none of 22(29%) 18(23%) 14(18%) 15(20%) 8(10%) 0(0%) 0(0%) 77

one of 590(50%) 118(10%) 212(18%) 75(6%) 169(14%) 12(1%) 0(0%) 1176

each of 58(49%) 21(18%) 9(8%) 16(13%) 11(9%) 1(1%) 3(2%) 119

either of 7(27%) 3(12%) 2(7%) 7(27%) 6(23%) 1(4%) 0(0%) 26

(a) few of 21(47%) 5(11%) 9(20%) 6(13%) 4(9%) 0(0%) 0(0%) 45

(a) little of 6(40%) 0(0%) 3(20%) 3(20%) 2(13%) 0(0%) 1(8%) 15

another of 6(35%) 3(17%) 4(24%) 0(0%) 3(17%) 0(0%) 1(6%) 17

certain of 4(33%) 1(8%) 3(25%) 1(8%) 2(17%) 0(0%) 1(8%) 12

some of 271(57%) 26(5%) 96(20%) 56(12%) 24(5%) 0(0%) 0(0%) 473

several of 17(57%) 2(7%) 6(20%) 4(13%) 1(3%) 0(0%) 0(0%) 30

half of 69(55%) 6(5%) 15(12%) 7(6%) 23(18%) 5(4%) 0(0%) 125

both of 4(6%) 7(10%) 6(8%) 34(47%) 21(29%) 0(0%) 0(0%) 72

many of 87(38%) 31(14%) 51(22%) 47(21%) 13(6%) 0(0%) 0(0%) 229

most of 171(49%) 15(4%) 67(19%) 60(17%) 35(10%) 0(0%) 0(0%) 348

all of 87(28%) 24(8%) 61(19%) 79(25%) 56(18%) 9(3%) 0(0%) 316

the rest of 106(62%) 5(3%) 28(16%) 20(12%) 12(7%) 0(0%) 0(0%) 171

the remainder of 17(71%) 2(8%) 3(13%) 0(0%) 2(8%) 0(0%) 0(0%) 24

the whole of 5(31%) 1(6%) 3(19%) 1(6%) 5(31%) 0(0%) 1(6%) 16

enough of 2(11%) 5(26%) 2(11%) 2(11%) 2(11%) 4(21%) 2(11%) 19

much of 82(41%) 17(8%) 35(17%) 12(6%) 21(11%) 24(12%) 9(5%) 200

more of 35(41%) 3(3%) 5(6%) 5(6%) 14(16%) 23(27%) 1(1%) 86

less of 0(0%) 0(0%) 1(8%) 0(0%) 2(17%) 9(75%) 0(0%) 12

a/the majority of 13(18%) 3(4%) 6(8%) 1(1%) 2(3%) 0(0%) 48(66%) 73

a/the minority of 1(25%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 3(75%) 4

a/the mass of 5(18%) 0(0%) 1(4%) 0(0%) 3(11%) 0(0%) 19(67%) 28

a/the part of 293(44%) 32(5%) 99(15%) 41(6%) 82(12%) 64(10%) 52(8%) 663

a/the portion of 61(61%) 7(7%) 9(9%) 2(2%) 7(7%) 3(3%) 11(11%) 100

a/the remnant of 3(30%) 0(0%) 1(10%) 0(0%) 1(10%) 2(20%) 3(30%) 10

a/the section of 22(42%) 6(12%) 0(0%) 0(0%) 10(19%) 3(6%) 11(21%) 52

a/the segment of 10(33%) 2(7%) 3(10%) 1(3%) 6(20%) 3(10%) 5(17%) 30

594 Yu Kyoung Shin

thedemon-strative

posses-sive

definite pron.

other a/an Ø Total

a couple of 5(5%) 2(2%) 1(1%) 1(1%) 0(0%) 0(0%) 98(91%) 107

a lot of 5(3%) 5(3%) 1(0.5%) 5(3%) 9(5%) 0(0%) 158(86%) 183

lots of 1(2%) 0(0%) 0(0%) 3(5%) 2(3%) 0(0%) 52(90%) 58

loads of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 3(100%) 3

plenty of 0(0%) 0(0%) 0(0%) 3(5%) 0(0%) 0(0%) 57(95%) 60

tons of 0(0%) 0(0%) 1(3%) 0(0%) 0(0%) 0(0%) 35(97%) 36

a heap of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 8(100%) 8

an abundance of 0(0%) 0(7%) 0(0%) 0(0%) 0(0%) 0(0%) 13(100%) 13

an amount of 2(2%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 108(98%) 110

a good/great deal of 4(7%) 0(0%) 2(4%) 0(0%) 3(5%) 0(0%) 47(84%) 56

a/the quantity of 3(7%) 0(0%) 0(0%) 0(0%) 0(0%) 1(3%) 36(90%) 40

a/the number of 17(2.6%) 8(1.2%) 8(1.2%) 8(1.2%) 2(0.3%) 1(0.1%) 617(95%) 661

Total 2,170(36%) 395(6%) 788(13%) 534(9%) 584(10%) 168(3%) 1,403(23%) 6,042

* other includes proper nouns, wh-words, reflexive pronouns, quantifications, and numbers.

* Ø refers to zero article.

* Percentages do not add up to 100 % because the numbers have been rounded off.


Appendix 2. Summary of speech sample

Corpus N Child Sex Age range

1. Bliss (1988) 6 Aimee F 5;4

Justin M 4;6

Marjorie F 2;3

Melissa F 3;4

Meredith F 2:5

Trevor M 4:3

2. Bloom (1970) 1 Peter M 1;9-3;1

3. Bohannon (1977) 1 Nathaniel M 3;0

4. Braunwald (1978) 1 Laura F 1;05-5:10

5. Brown (1973) 3 Adam M 2;3-5;2

Eve F 1;6-2;3

Sarah F 2;3-5;1

6. Clark (1978) 1 Shem M 2;2-3;2

7. Cornell (Unidentified) 2 Geraldine Unidentified 1;6-2;5

Peter M 5;0

8. Demetras (1989) 4 Trevor M 2;0-3;11

Jimmy M 2;2-2;9

Michael M 2;2

Tim M 2;1-2;2

9. Menn & Feldman (2001) 1 Steven M 1;2-2;3

10. Garvey (1979) 25 Ann Unidentified 4;0

Wes U 4;1

Flo U 5;1

Guy U 5;2

Bud U 5;1

Zoe U 5;0

Fay U 5;3

Jay U 5;3

Meg U 5;0

Glo U 5;1

Joy U 4;9

Ida U 5;1

Kay U 3;6

Deb U 3;7

Gay U 5;2

Nan U 2;10

596 Yu Kyoung Shin


10. Garvey (1979) Ima U 5;4

Ned U 5;2

Ivy U 4;9

Pat U 4;10

Pia U 4;9

Kim U 3;0

Roy U 3;2

Ava U 3;2

Val U 4;7

11. Gathercole (1980) 14 Jeff U 2;9/2;11

Megan U 3;6/3;9/3;10/4;0

Sarah U 3;6/3;9/3;10/4;0

Lily U 3;10/4;2/4;3/4;5

Michael U 2;10/3;0/3;3/3;4

Erin U 2;10/3;0/3;3/3;4

Matthew U 4;7/4;10/4;11/5;1

Saasha U 5;8/6;0/6;1/6;2

Gillian U 4;3/4;6/4;7/4;9

Erik U 4;6

Luke U 4;11/5;2/5;3/5;5

Brian U 5;11

Nicole U 5;4/5;7/5;9/5;10

Eric U 3;2/3;3

12. Gleason (1980) 23 Andy M 4;0/4;1/4;2

Bobby M 4;1/4;2/4;4

Charlie M 2;11/3;0

David M 4;1/4;2

Guy M 3;0-3;1

Edward M 4;3/4;4

Helen F 4;4/4;11

Isadora F 3;6/3;7

John M 4;1/4;2

Katie F 3;2

Laurel F 2;11/3;0

Martin M 2;5/2;6

Olivia F 3;2/3;3

Patricia F 2;5/2;6



12. Gleason (1980) Susan F 3;2

Theresa F 4;0/4;2

Victor M 2;3/2;5

Wanda F 3;11/4;0

Frank M 5;2

Richard M 2;8/2;9

Ursula F 3;7

William M 2;2/2;3

Xavia M 4;0/4;3

13. Haggerty (1929) 1 Helen F 4;4/4;11

14. Hall (1984) 37

JOB, JUB, MAA, ROB,

TOH, TOS, ZOR, ANC,

BOM, BRD, CHJ, DED,

JAF, KIF, MIM, REF,

TRH, VOH, BOO, BRH,

DAL, KAG, KAO,

MIG, SAT, STL, SUT,

ANL, DEG, KIG, KMF,

LEF, MIS, PAG, ROG,

ROJ, TRC

U 4;6-5;0

15. Dickinson & Tabors (2001) 100 Unidentified F+M 2-5

16. Kuczaj (1977) 1 Abe M 2;4-5;0

17. MacWhinney (2000) 1 Ross M 1;4-5;11

18. Nelson (1989) 1 Emily F 1;9-3;0

19. Peters (1987) 1 Seth M 1;3-2;1

20. Post (1992) 3 Lew F 1;10-2;8

She F 1;7-2;5

Tow F 1;7-2;5

21. Demuth, Culbertson, & 6 Alex M 1;5-3;5

Alter (2006) Ethan M 0;11-2;11

Lily F 1;1-4;0

Naima F 0;11-3;10

Violet F 1;2-3;11

William M 1;4-3;4

22. Sachs (1983) 1 Naomi F 1;2-4;9

23. Sawyer (Unidentified) 19 Artie M 5;4

Alicia F 4;0

Anne F 4;1

Aretha F 3;9

598 Yu Kyoung Shin


23. Sawyer (Unidentified) Bernie M 4;1

Corinna F 5;9

Eddy M 4;3

Jerry M 3;8

Jan F 5;2

Karl M 5;9

Kim F 4;2

Matt M 4;9

Mikey M 4;1

Mark M 4;4

Muhammed M 4;8

Ned M 5;1

Rachel F 4;3

Sam M 4;9

Yung-soo F 5;5

24. Snow (MacWhinney 2000) 1 Nathaniel M 2;5-3;9

25. Suppes (1974) 1 Nina F 1;1-3;3

26. Valian (1981) 21 Unidentified F+M 1;9-2;5

27. Van Houten (1986) 27 Brooke F 2;4/3;5

Erica F 2;4/3;4

Danielle F 2;4/3;6

Jarrett M 2;4/3;4

Shawna F 2;4

David M 2;4

Jessica F 2;4/3;4

Stephen M 2;4/3;4

Nicole F 2;4/3;4

Kimberly F 2;4

Brian M 2;4/3;4

Kevin M 2;4/3;5

Adam M 2;4/3;3

Aaron M 2;4/3;5

Anthony M 2;4/3;3

Adam M 2;4/3;4

Matthew M 2;4/3;4

Peter M 2;4/3;2

Tommy M 2;4/3;4



27. Van Houten (1986) Robert M 2;4/3;5

Sarah F 2;4/3;3

Christopher M 3;7

Jeffrey M 3;4

Nicolette F 3;3

Jill F 3;6

Benny M 3;5

Tristan M 3;4

28. Van Kleek (Unidentified) 20 Amy F 3;9

Andrew M 4;0

Ben M 3;8

Bree M 3;10

Brent M 3;9

Chris M 3;10

David M 4;0

Graham M 3;9

Jenny F 3;1

Jessica F 3;8

Justin M 3;10

Lara F 4;0

Matjoy F 3;11

Mattm M 3;11

Megan F 3;11

Nikki F 3;6

Rachel F 3;8

Shea M 3;8

Susan M 3;7

Walter M 4;0

29. Warren-Leubecker (1982) 17 Alfred M 2;6

Allen M 2;3

Beth F 4;9

Carol F 2;6

David M 5;10

Doug M 2;7

George M 4;11

Gina F 3;1

Jeff M 1;9

600 Yu Kyoung Shin


29. Warren-Leubecker (1982) Jmarkey M 5;3

John M 5;9

Katie F 2;4

Lousie F 5;3

Mary F 4;6

Megan F 1;6

Scott M 1;7

Wendy F 2;0

30. Weist & Zevenbergen (2008) 6 Benjamin M 2;4-3;3

Emily F 2;06-4;5

Emm U 2;7-4;8

Jill U 2;1-2;10

Mat U 2;3-5;0

Rom U 2;2-4;7

Note: U refers to Unidentified.



quantifiers at age one in CHILDES

thedemonstra-

tiveposses-

sivedefinitepron.

other a/an Ø Total

any of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

none of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

one of 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1

each of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

either of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) few of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) little of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

another of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

some of 0(0%) 2(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 2

half of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

both of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

many of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

most of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

all of 2(50%) 1(25%) 0(0%) 1(25%) 0(0%) 0(0%) 0(0%) 4

the rest of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

enough of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

much of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

more of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

a/the part of 1(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1

a couple of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

a lot of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1(100%) 1

lots of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 4(100%) 4

plenty of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1(100%) 1

tons of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

Total 3(21.4%) 4(28.6%) 0(0%) 1(7.1%) 0(0%) 0(0%) 6(42.9%) 14




602 Yu Kyoung Shin


quantifiers at age two in CHILDES

thedemonstra-

tiveposses-

sivedefinitepron.

other a/an Ø Total

any of 1(20%) 2(40%) 0(0%) 1(20%) 1(20%) 0(0%) 0(0%) 5

none of 2(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 2

one of 19(20.4%) 41(44.1%) 16(17.2%) 11(11.8%) 1(1.1%) 1(1.1%) 4(4.3%) 93

each of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

either of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) few of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) little of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

another of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

some of 7(13.5%) 14(26.9%) 18(34.6%) 6(11.5%) 6(11.5%) 0(0%) 1(1.9%) 52

half of 1(20%) 0(0%) 2(40%) 2(40%) 0(%) 0(0%) 0(0%) 5

both of 1(5.6%) 0(0%) 1(5.6%) 15(83.3%) 1(5.6%) 0(0%) 0(0%) 18

many of 0(0%) 0(0%) 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 1

most of 1(50%) 0(0%) 0(0%) 1(50%) 0(0%) 0(0%) 0(0%) 2

all of 11(18.3%) 4(6.7%) 6(10%) 37(61.7%) 1(1.7%) 0(0%) 1(1.7%) 60

the rest of 8(36.4%) 0(0%) 1(4.5%) 13(59%) 0(0%) 0(0%) 0(0%) 22

enough of 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1

much of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

more of 0(0%) 1(50%) 0(0%) 0(0%) 0(0%) 1(50%) 0(0%) 2

a/the part of 25(52.1%) 4(8.3%) 6(12.5%) 3(6.2%) 1(2%) 3(6.2%) 6(12.5) 48

a couple of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 27(100%) 27

a lot of 0(0%) 0(0%) 0(0%) 3(3.6%) 1(1.1%) 0(0%) 80(95.2%) 84

lots of 0(0%) 1(2.1%) 0(0%) 3(6.3%) 1(2.1%) 0(0%) 42(89.3) 47

plenty of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

tons of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

Total 76(16.2%) 68(14.5%) 50(10.7%) 96(20.5%) 13(2.8%) 5(1.1%) 161(34.3%) 469






quantifiers at age three in CHILDES

thedemon- strative

posses-sive

definitepron.

other a/an Ø Total

any of 1(6.7%) 7(46.7%) 3(20%) 4(26.7%) 0(0%) 0(0%) 0(0%) 15

none of 0(0%) 0(0%) 0(0%) 2(100%) 0(0%) 0(0%) 0(0%) 2

one of 30(19.7%) 50(32.9%) 37(24.3%) 22(14.5%) 11(7.2%) 1(0.7%) 1(0.7%) 152

each of 0(0%) 0(0%) 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 1

either of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) few of 0(0%) 0(0%) 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 1

(a) little of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

another of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

some of 14(17.3%) 15(18.5%) 16(19.8%) 26(32.1%) 5(6.2%) 0(0%) 5(6.2%) 81

half of 1(14.3%) 0(0%) 0(0%) 1(14.3%) 1(14.3%) 4(57.1%) 0(0%) 7

both of 1(5%) 0(0%) 2(10%) 17(85%) 0(0%) 0(0%) 0(0%) 20

many of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

most of 2(33.3%) 0(0%) 0(0%) 1(16.7%) 2(33.3%) 0(0%) 1(16.7%) 6

all of 28(19.7%) 16(11.3%) 14(9.9%) 81(%) 1(57%) 0(0%) 2(1.4%) 142

the rest of 9(28.1%) 0(0%) 3(9.4%) 19(59.4%) 0(0%) 0(0%) 1(3.1%) 32

enough of 1(50%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1(50%) 2

much of 0(0%) 0(0%) 0(0%) 2(100%) 0(0%) 0(0%) 0(0%) 2

more of 1(8.3%) 2(16.7%) 2(16.7%) 1(8.3%) 0(0%) 0(0%) 2(16.7%) 8

a/the part of 17(25.8%) 1(1.5%) 15(22.7%) 6(9.1%) 3(4.5%) 11(16.7%) 6(9.1%) 59

a couple of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 6(100%) 6

a lot of 2(1.3%) 1(0.7%) 0(0%) 7(4.6%) 1(0.7%) 0(0%) 140(93%) 151

lots of 0(0%) 0(0%) 2(1.8%) 3(2.6%) 1(0.9%) 0(0%) 108(95%) 114

plenty of 0(0%) 0(0%) 0(0%) 0(0%) 2(40%) 0(0%) 3(60%) 5

tons of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1(100%) 1

Total 107(13.3%) 92(11.4%) 94(11.6%) 194(24%) 27(3.3%) 16(2%) 277(34.3%) 807




604 Yu Kyoung Shin


quantifiers at age four in CHILDES

thedemon-

strative

posses-

sive

definite

pron.other a/an Ø Total

any of 0(0%) 1(9.1%) 3(27.3%) 6(54.5%) 0(0%) 0(0%) 1(9.1%) 11

none of 1(%) 2(%) 2(%) 5(%) 1(%) 0(0%) 0(0%) 11

one of 40(23.5%) 47(27.6%) 35(20.6%) 31(18.2%) 11(6.5%) 1(0.6%) 5(3%) 170

each of 1(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1

either of 1(33.3%) 0(0%) 0(0%) 2(66.7%) 0(0%) 0(0%) 0(0%) 3

(a) few of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) little of 0(0%) 0(0%) 0(0%) 3(60%) 0(0%) 0(0%) 2(40%) 5

another of 11(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 11

some of 20(24.1%) 11(13.3%) 18(21.7%) 29(35%) 3(3.6%) 0(0%) 2(2.4%) 83

half of 0(0%) 0(0%) 1(7.1%) 8(57.1%) 1(7.1%) 3(21.4%) 1(7.1%) 14

both of 0(0%) 2(3%) 8(12.1%) 53(80.3%) 2(3%) 0(0%) 1(1.5%) 66

many of 0(0%) 1(25%) 0(0%) 3(75%) 0(0%) 0(0%) 0(0%) 4

most of 3(20%) 0(0%) 2(13.3%) 6(40%) 3(20%) 0(0%) 1(6.7%) 15

all of 23(12.5%) 15(8.2%) 12(6.5%) 130(70.7%) 1(0.5%) 0(0%) 3(1.6%) 184

the rest of 8(33.3%) 0(0%) 6(25%) 9(38%) 1(4.2%) 0(0%) 0(0%) 24

enough of 0(0%) 0(0%) 0(0%) 1(50%) 0(0%) 0(0%) 1(50%) 2

much of 0(0%) 0(0%) 0(0%) 2(66.7%) 0(0%) 0(0%) 1(33.3%) 3

more of 2(20%) 3(30%) 2(20%) 2(20%) 0(0%) 0(0%) 1(10%) 10

a/the part of 24(41.4%) 3(5.2%) 9(15.5%) 14(24.1%) 2(3.4%) 2(3.4%) 4(6.9%) 58

a couple of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 4(100%) 4

a lot of 2(1.3%) 1(0.6%) 2(1.3%) 12(7.7%) 0(0%) 0(0%) 139(89%) 156

lots of 0(0%) 0(0%) 0(0%) 6(9.7%) 0(0%) 0(0%) 56(90%) 62

plenty of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 3(100%) 3

tons of 0(0%) 0(0%) 0(0%) 0(0%) 1(33.3%) 0(0%) 2(66.7%) 3

Total 136(15.1%) 86(9.5%) 100(11.1%) 322(35.7%) 26(2.9%) 6(0.7%) 227(24.3%) 903






quantifiers at age five in CHILDES

thedemon-

strative

posses-

sive

definite


any of 1(25%) 1(25%) 1(25%) 1(25%) 0(0%) 0(0%) 0(0%) 4

none of 2(66.7%) 0(0%) 0(0%) 1(33.3%) 0(0%) 0(0%) 0(0%) 3

one of 19(28.8%) 21(31.8%) 13(19.7%) 8(12.1%) 5(7.6%) 0(0%) 0(0%) 66

each of 0(0%) 0(0%) 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 1

either of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) few of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

(a) little of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

another of 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1

some of 7(18%) 7(18%) 5(12.8%) 16(41%) 3(7.7%) 1(2.6%) 0(0%) 39

half of 0(0%) 0(0%) 0(0%) 3(75%) 0(0%) 1(25%) 0(0%) 4

both of 0(0%) 1(12.5%) 2(25%) 5(63%) 0(0%) 0(0%) 0(0%) 8

many of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1(100%) 1

most of 1(14.3%) 0(0%) 0(0%) 6(85.7%) 0(0%) 0(0%) 0(0%) 7

all of 16(21.9%) 3(4%) 2(2.7%) 46(63%) 2(2.7%) 2(2.7%) 2(2.7%) 73

the rest of 10(62.5%) 2(12.5%) 2(12.5%) 1(6.3%) 0(0%) 0(0%) 1(6.3%) 16

enough of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

much of 0(0%) 2(100%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 2

more of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

a/the part of 5(31.3%) 1(6.3%) 4(25%) 3(31.3%) 1(6.3%) 1(6.3%) 1(6.3%) 16

a couple of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 3(100%) 3

a lot of 0(0%) 0(0%) 0(0%) 1(3.7%) 0(0%) 0(0%) 26(96%) 27

lots of 1(2.7%) 0(0%) 0(0%) 1(2.7%) 0(0%) 0(0%) 35(95%) 37

plenty of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 1(100%) 1

tons of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0

Total 62(20.1%) 39(12.6%) 29(9.4%) 93(30.1%) 11(3.6%) 5(1.6%) 70(22.7%) 309



* Percentages do not add up to 100% because the numbers have been rounded off.

606 Yu Kyoung Shin


quantifiers at ages one to five in CHILDES

thedemon-

strative

posses-

sive

definite


any of 3(8.6%) 11(31.4%) 7(20%) 12(34.3%) 1(2.9%) 0(0%) 1(2.9%) 35

none of 5(2.8%) 2(11.1%) 2(11.1%) 8(44.4%) 1(5.6%) 0(0%) 0(0%) 18

one of 108(22.4%)160(33.2%) 101(21%) 72(15%) 28(5.8%) 3(0.6%) 10(2.1%) 482

each of 1(33.3%) 0(0%) 0(0%) 2(66.7%) 0(0%) 0(0%) 0(0%) 3

either of 1(33.3%) 0(0%) 0(0%) 2(66.7%) 0(0%) 0(0%) 0(0%) 3

(a) few of 0(0%) 0(0%) 0(0%) 1(100%) 0(0%) 0(0%) 0(0%) 1

(a) little of 0(0%) 0(0%) 0(0%) 3(60%) 0(0%) 0(0%) 2(40%) 5

another of 11(91.7%) 1(8.3%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 12

some of 48(18.7%) 49(19%) 57(22.2%) 77(30%) 17(6.6%) 1(0.4%) 8(1.2%) 257

half of 2(6.7%) 0(0%) 3(10%) 14(46.7%) 2(6.7%) 8(26.7%) 1(3.3%) 30

both of 2(1.8%) 3(2.7%) 13(11.6%) 90(80.4%) 3(2.7%) 0(0%) 1(0.9%) 112

many of 0(0%) 1(16.7%) 0(0%) 4(66.7%) 0(0%) 0(0%) 1(16.7%) 6

most of 7(23.3%) 0(0%) 2(6.7%) 14(46.7%) 5(16.7%) 0(%) 2(6.7%) 30

all of 80(17.3%) 39(8.4%) 34(7.3%) 295(63.7%) 5(1.1%) 2(0.4%) 8(1.7%) 463

the rest of 35(37.2%) 2(2.1%) 12(12.8%) 42(44.7%) 1(1.1%) 0(0%) 2(2.1%) 94

enough of 1(20%) 1(20%) 0(0%) 1(20%) 0(0%) 0(0%) 2(40%) 5

much of 0(0%) 2(28.6%) 0(0%) 4(57.1%) 0(0%) 0(0%) 1(14.3%) 7

more of 3(15%) 6(30%) 4(20%) 3(15%) 0(0%) 1(5%) 3(15%) 20

a/the part of 72(39.6%) 9(4.9%) 34(18.7%) 26(14.3%) 7(3.8%) 17(9.3%) 17(9.3%) 182

a couple of 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 0(0%) 40(100%) 40

a lot of 4(1%) 2(0.5%) 2(0.5%) 23(5.5%) 2(0.5%) 0(0%) 386(92%) 419

lots of 1(0.4%) 1(0.4%) 2(0.8%) 13(5%) 2(0.8%) 0(0%) 245(93%) 264

plenty of 0(0%) 0(0%) 0(0%) 0(0%) 2(20%) 0(0%) 8(80%) 10

tons of 0(0%) 0(0%) 0(0%) 0(0%) 1(25%) 0(0%) 3(75%) 4

Total 384(18.7%) 289(14.1%) 273(13.3%) 706(34.4%) 77(3.8%) 32(1.6%) 741(36%) 2,502





Appendix 9. Det(erminer)-obligatory


a/an Ø Totalthe

demon-

strative

posses-

sive

definite

pron.other Total

AGE 12

(28.5%)

4

(57.1%)

0

(0%)

1

(14.3%)

0

(0%)

7

(100%)

0

(0%)

0

(0%)

7

(100%)

AGE 251

(19.6%)

61

(23.5%)

44

(16.9%)

87

(33.5%)

10

(3.8%)

253

(97.3%)

1

(0.4%)

6

(2.3%)

260

(100%)

AGE 386

(18.7%)

88

(19.2%)

75

(16.3%)

175

(38.1%)

20

(4.4%)

444

(96.7%)

5

(1.1%)

10

(2.2%)

459

(100%)

AGE 4108

(18%)

79

(13.1%)

87

(14.5%)

285

(47.3%)

23

(3.8%)

582

(96.7%)

4

(0.7%)

16

(2.6%)

602

(100%)

AGE 556

(25.1%)

36

(16.1%)

25

(11.2%)

88

(39.5%)

10

(4.5%)

215

(96.4%)

4

(1.8%)

4

(1.8%)

223

(100%)

AGE

1 to 5

303

(19.5%)

268

(17.3%)

231

(14.9%)

636

(41%)

63

(4.1%)

1,501

(96.8%)

14

(0.9%)

36

(2.3%)

1,551

(100%)

ADULTS1,606

(46.8%)

305

(8.9%)

613

(17.9%)

450

(13.2%)

418

(12.1%)

3,392

(98.9%)

31

(0.9%)

7

(0.2%)

3,430

(100%)

Appendix 10. Contextual


a/an Ø Totalthe

demon-

strative

posses-

sive

definite

pron.other Total

AGE 11

(100%)

0

(0%)

0

(0%)

0

(0%)

0

(0%)

1

(100%)

0

(0%)

0

(0%)

1

(100%)

AGE 225

(49%)

6

(11.8%)

6

(11.8%)

3

(5.9%)

1

(1.9%)

41

(80.4%)

4

(7.8%)

6

(11.8%)

51

(100%)

AGE 319

(26.8%)

3

(4.2%)

17

(23.9%)

9

(12.7%)

3

(4.2%)

51

(71.8%)

11

(15.5%)

9

(12.7%)

71

(100%)

AGE 426

(25.6%)

6

(8.2%)

11

(15.1%)

19

(26%)

2

(2.8%)

64

(87.7%)

2

(2.7%)

7

(9.6%)

73

(100%)

AGE 55

(27.8%)

3

(16.7%)

4

(22.2%)

3

(16.7%)

1

(5.6%)

16

(88.9%)

1

(5.6%)

1

(5.6%)

18

(100%)

AGE

1 to 5

76

(35.5%)

18

(8.4%)

38

(17.8%)

34

(15.9%)

7

(3.3%)

173

(80.9%)

18

(8.4%)

23

(10.7%)

214

(100%)

ADULTS527

(41.3%)

75

(5.9%)

162

(12.7%)

64

(5%)

150

(11.7%)

978

(76.6%)

135

(10.6%)

164

(12.8%)

1,277

(100%)

608 Yu Kyoung Shin

Appendix 11. Zero-likely

Definite Determiners

a/an Ø Totalthe

demon-

strative

posses-

sive

definite

pron.other Total

AGE 10

(0%)

0

(0%)

0

(0%)

0

(0%)

0

(0%)

0

(0%)

0

(0%)

6

(100%)

6

(100%)

AGE 20

(0%)

1

(0.6%)

0

(0%)

6

(3.8%)

2

(1.3%)

9

(5.7%)

0

(0%)

149

(94.3%)

158

(100%)

AGE 32

(0.7%)

1

(0.4%)

2

(0.7%)

10

(3.6%)

4

(1.4%)

19

(6.9%)

0

(0%)

258

(93.1%)

277

(100%)

AGE 42

(0.9%)

1

(0.4%)

2

(0.9%)

18

(7.9%)

1

(0.4%)

24

(10.5%)

0

(0%)

204

(89.5%)

228

(100%)

AGE 51

(1.5%)

0

(0%)

0

(0%)

2

(2.9%)

0

(0%)

3

(4.4%)

0

(0%)

65

(95.6%)

68

(100%)

AGE

1 to 5

5

(0.7%)

3

(0.4%)

4

(0.5%)

36

(4.9%)

7

(1%)

55

(7.5%)

0

(0%)

682

(92.5%)

737

(100%)

ADULTS37

(2.7%)

15

(1.1%)

13

(1%)

20

(1.5%)

16

(1.2%)

101

(7.5%)

2

(0.2%)

1,232

(92.3%)

1,335

(100%)

Yu Kyoung Shin

Department of English

Sogang University

1 Shinsu-dong, Mapo-gu, Seoul 121-742, Korea

Email: [email protected]

Received: October 14, 2012

Revised version received: December 10, 2012

Accepted: December 16, 2012

a new look at determiners in early grammar: phrasal...

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