CLEAR Dec 2012

CLEAR Dec 2012

Volume-1 Issue-2

CLEAR Magazine

(Computational Linguistics in

Engineering And Research)

M. Tech Computational Linguistics

Dept. of Computer Science and

Engineering

Govt. Engineering College,

Sreekrishnapuram, Palakkad

678633

simplequest.in@gmail.com

Chief Editor

Dr. P. C. Reghu Raj

Professor and Head

Dept. of Computer Science and

Engineering

Govt. Engineering College,

Sreekrishnapuram, Palakkad

Editors

Manu Madhavan

Robert Jesuraj. K

Athira P M

Cover page and Layout

Mujeeb Rehman. O

Fuzzy Logic Applications in Natural

Language processing

…Our understanding of most physical processes is based

largely on imprecise human reasoning. This imprecision

(when compared to the precise quantities required by

computers) is nonetheless a form of information that can be

quite useful to humans…. 1

Indic Language Computing: A Review

….But with almost three dozen major languages and

hundreds of dialects, the task is more complex in India. The

tools present in the global market cannot be replicated

owing to the complexity of multiple languages that exist in

the country….. 7

Natural Language Processing and Human

Computer Interaction

……With data mining, Wal-Mart was able to figure out that

diapers and beer were bought together. This allowed them

to position those two groceries closer together. We can see

that a normal human would not be able to…….. 11

Google’s Driverless Car.

…….The Google car project team was working in secret in

plain view on vehicles that can drive themselves, using

artificial-intelligence software that can sense anything near

the car and mimic the decisions made by a human driver.

With someone behind the wheel to take control……. 17

GNU Octave

…a tool for numerical calculations and solving numerical

problems … 21

CLEAR Dec 2012

CLEAR Dec 2012

Dear Readers!

Welcome back to the world of Computational Linguistics. This edition of

CLEAR brings to you some insight into current trends in Indian Language

Computing, Fuzzy logic applications etc. It is heartening to note that

better recognition of the importance of language processing using

computational means is visible among the computing community. Our

interaction with various academic and R&D organizations of repute in the

country definitely show the emergence of new applications of CL, ASR,

etc. in implementing better HCI modules. This has given us esh energy to

work harder. At the same time, it was a disappointment to see that the

response to our call for a national conference on CL and IR did not attract

attention of the research community in this field. This points to the big

gap between the demand and supply of ideas and people in CL/NLP. It is

this gap that CLEAR aims to reduce.

The CLEAR team wishes all the readers a Merry Christmas and a

prosperous year ahead!

Sincerely,

Reghu Raj

CLEAR Dec 2012 1

Fuzzy Logic Applications in Natural Language Processing

Fuzzy Logic has

widespread applications in

the field of natural

language processing. Here

we also discuss a fuzzy

logic based natural

language processing

system for speech

recognition, and a fuzzy

logic based term weighting

scheme used for

information extraction. We

also discuss how fuzzy

logic and fuzzy reasoning

are used to deal with

uncertainty information in

Panini's Sanskrit

Grammar.

Fuzzy logic is an approach to

computing based on degrees

of truth rather than the usual

true or false (1 or 0) Boolean

logic on which the modern

computer is based. Natural

language (like most other

activities in life) is not easily

translated into the absolute

terms of 0 and 1. Fuzzy logic

includes 0 and 1 as extreme

cases of truth but also

includes the various states of

truth in between. Fuzzy logic

deals mathematically with

imprecise information usually

employed by humans.

Fuzzy Logic has widespread

applications in the field of

natural language processing.

We discuss some applications

of fuzzy logic in NLP. Lotfi A

Zadeh's work on Computing

with Words is an important

application of fuzzy logic in

natural language processing.

Here we also discuss a fuzzy

logic based natural language

processing system for speech

recognition, and a fuzzy logic

Author

Divya S

M. Tech Computational Linguistics Govt. Engineering College,

Sreekrishnapuram Palakkad

Palakkad

based term weighting scheme

used for information extraction.

We also discuss how fuzzy logic

and fuzzy reasoning are used to

deal with uncertainty

information in Panini's Sanskrit

Grammar.

Fuzzy Logic

Our understanding of most

physical processes is based

largely on imprecise human

reasoning. This imprecision

(when compared to the precise

quantities required by

computers) is nonetheless a

form of information that can be

quite useful to humans. The

ability to embed such reasoning

in hitherto intractable and

complex problems is the

criterion by which the efficiency

of fuzzy logic is judged.

Undoubtedly this ability cannot

solve problems that require

precision. But not many human

problems require such precision

problems such as parking a car,

backing up a trailer, navigating

a car among others on a

freeway, washing clothes,

controlling traffic at

intersections, judging beauty

contests and a

CLEAR Dec 2012 2

preliminary understanding of a

complex system. And for such

problems Fuzzy logic takes the

focus.

Fuzzy logic resembles human

decision making with an ability to

generate precise solutions from

certain or approximate information.

It fills an important gap in

engineering design methods left

vacant by purely mathematical

approaches (e.g. linear control

design), and purely logic-based

approaches (e.g. expert systems) in

system design.

Fuzzy Logic allows something to be

partially true and partially false. A

simple example follows: Is a man

who stands 170 centimeters (5‘6")

considered to be tall? Traditionally

we must define a threshold over

which a man of a certain height is

considered a member of the tall set

and under which he is not. Fuzzy

Logic allows one to speak of a 170

cm man as both a member of the

tall set and the medium set, and

possibly even the short set. He may

be considered to a larger degree a

member of the medium set than he

is of the tall set. A man who stands

190 centimeters will be to a higher

degree a member of the tall set. If a

problem suggests there is some

consequence

related to the height of a tall man,

consequence related to the height of a

tall man, then the consequence can be

applied or inferred in relation to his

degree of membership in the tall set.

Basically, Fuzzy Logic (FL) is a

multivalve logic that allows intermediate

values to be defined between

conventional evaluations like true/false,

yes/no, high/low, etc. Notions like

rather tall or very fast can be

formulated mathematically and

processed by computers, in order to

apply a more human-like way of

thinking in the programming of

computers.

Fuzzy Logic can be used to generate

solutions to problems based on "vague,

ambiguous, qualitative, incomplete or

imprecise information. The use of fuzzy

logic is an effective alternative in

natural language analysis compared to

statistical and other approaches. It is

commonly recognized that many

phenomena in natural language lend

themselves to descriptions by fuzzy

mathematics, including fuzzy sets, fuzzy

relations and fuzzy logic.

Fuzzy logic deals mathematically with

imprecise information usually employed

by humans. When considering the use

of fuzzy logic for a given problem, an

engineer or scientist should ponder the

need for exploiting the tolerance for

imprecision.

Fuzzy Set and Crisp Set

The universe of discourse is

the universe of all available

information on a given

problem. Once this universe

is defined it is able to define

certain events on this

information space. Sets are

described as mathematical

abstractions of these events

and of the universe itself. A

classical set is defined by

crisp boundaries, i.e., there

is no uncertainty in the

prescription or location of the

boundaries of the set, as

shown in Fig. 3.1a where the

boundary of crisp set A is an

unambiguous line. In figure

3.1a, point a is clearly a

member of crisp set A; point

b is unambiguously not a

member of set A.

Figure 1(a): Crisp Set (b)

Fuzzy set

A fuzzy set, on the other

hand, is prescribed by vague

or ambiguous properties;

CLEAR Dec 2012 3

Fuzzy Set A is represented as A. The

shaded boundary represents the

boundary region of A. In the central

(unshaded) region of the fuzzy set,

point a is clearly a full member of

the set. Outside the boundary region

of the fuzzy set, point b is clearly not

a member of the fuzzy set. However,

the membership of point c, which is

on the boundary region, is

ambiguous. If complete membership

in a set (such as point a in Fig. 3.1b)

is represented by value 1, and non-

membership in a set (such as point b

in Fig. 3.1b) is represented by 0,

then point c in Fig.3.1b must have

some intermediate value of

membership (partial membership in

fuzzy set A) on the interval [0,1].

Presumably the membership of point

c in A approaches a value of 1 as it

moves closer to the central

(unshaded) region of A, and the

membership of point c in A

approaches a value of 0 as it moves

closer to leaving the boundary region

of A. Fuzzy sets cover virtually all of

the definitions, precepts, and axioms

that define classical sets. Crisp sets

are a special form of fuzzy sets; they

are sets without ambiguity in their

membership (i.e., they are sets with

unambiguous boundaries).

In classical, or crisp, sets the

transition for an element in the

universe between membership and

non-membership in a given set is

abrupt and well defined. For an

element in a universe that contains

fuzzy sets, this transition can be

gradual. This transition among various

degrees of membership can be

thought of as conforming to the fact

that the boundaries of the fuzzy sets

are vague and ambiguous. Hence,

membership of an element from the

universe in this set is measured by a

function that attempts to describe

vagueness and ambiguity. If an

element in the universe, say x, is a

member of fuzzy set A then this

mapping is given by µA (x) ε [0,1].

Fuzzy Logic and NLP

Computing with words, is a

methodology in which the objects of

computation are words and

propositions drawn from a natural

language, e.g., small, large, far,

heavy, not very likely, Berkeley is

near San Francisco, etc. Computing

with words is inspired by the

remarkable human capability to

perform a wide variety of physical and

mental tasks without any

measurements and any computations.

Underlying this remarkable

capability is the brains crucial

ability to manipulate perceptions

of distance, size, weight, color,

speed, time, direction, force,

number, truth, likelihood and

other characteristics of physical

and mental objects. Manipulation

of perceptions plays a key role in

human recognition, decision and

execution processes. Computing

with words provides a foundation

for a computational theory of

perceptions a theory which may

have an important bearing on

how humans make and machines

might make perception-based

rational decisions in an

environment of imprecision,

uncertainty and partial truth.

A basic difference between

perceptions and measurements

is that, in general, measurements

are crisp whereas perceptions are

fuzzy. To deal with perceptions it

is necessary to employ a logical

system that is fuzzy rather than

crisp. The computational theory of

perceptions, or CTP for short, is

based on the methodology of

computing with words (CW).

CLEAR Dec 2012 4

In CTP, words play the role of

labels of perceptions and, more

generally, perceptions are

expressed as propositions in a

natural language. CW-based

techniques are employed to

translate propositions expressed in

a natural language into what is

called the Generalized Constraint

Language (GCL).Fuzzy logic has

been successfully applied to the

description of words meanings as

related to language external

phenomena [4]. Another case of

fuzzy application is natural

language-driven database search.

Here the semantics of words can be

expressed as fuzzy membership

functions for certain database

search keys [Medina, Vila]. A

language internal fuzzy treatment

is found in [Subasic], in which

affect types of certain words in

documents are dealt with as fuzzy

sets. Words representing emotions

are mapped to these fuzzy sets.

The difference between this case

and the previous two is that the

latter dealt with language internal

fuzzy phenomena.

Fuzzy Logic in Speech Recognition Fuzzy Logic has many applications in

Natural Language Processing. Fuzzy

Logic based NLP system can learn from

a linguistic corpus the fuzzy semantic

relations between the concepts

represented by words and use such

relations to process the word

sequences generated by speech

recognition systems [2]. Fuzzy logic

has also been successfully applied to

the description of words meanings as

related to language external

phenomena. Also Fuzzy linguistic

descriptors have been used in control

systems, in which mappings can be

established between fuzzy linguistic

terms and physical quantities. Hot,

cold, for example, can serve as labels

for fuzzy sets to which temperature

readings can be mapped into

membership degrees. Fuzzy logic rules

for control systems can accept fuzzy

descriptors in both the premises and

the consequents to simulate human-

like inference.

The main goal of a speech recognition

system is efficient processing of speech

recognition output.

Speech recognition system is

applied on restricted domains.

This means the vocabulary size

and senses and syntactic

constructs are restricted. Here

are some often-encountered

phenomena in a domain-

constrained speech system.

Out-of-vocabulary words.

A user may speak words

that are not contained in

the system lexicon.

Speech recognizer errors.

This may match a word

into a wrong word, insert

or delete a word, etc.

Flexible structures. The

user may use expressions

that the system's

grammar does not cover.

Disfluency. False start,

re-phrasing, repeated

words, mis-pronounced

words, half-pronounced

words, filled pauses, etc.

These could make the

system confused about

word semantic relations.

Fuzzy Logic based NLP system can learn from a linguistic corpus the fuzzy semantic

relations between the concepts represented by words and use such relations to process the

word sequences generated by speech recognition systems

CLEAR Dec 2012 5

Fuzzy Logic Based Term

weighting

Term weighting (TW) is one of the

major challenges in IE and IR. The

values of the weights must be

related somehow to the importance

of an index term in its corresponding

set of knowledge in this case, Topic,

Section or Object. In FL based term

weighting scheme every index term

has an associated weight. This

weight has a value between 0 and 1

depending on the importance of term

in every hierarchy level. Greater

importance means higher weight. A

FL engine is used to determine the

degree of certainty or importance of

a document for a given query. Index

term weight for every level act as

input to FL Engine. Output of FL

Engine is the Degree of certainty. If

degree of certainty lowers than a

certain threshold; content is

rejected.

In this method, the whole set of

knowledge, which constitutes the

hierarchic level 0, is divided into

level 1 subsets. For each level 1

subset, index terms must have

certain weights, which are the

possible inputs to an FL engine. If

the degree of certainty

corresponding to a subset is lower

than a predefined value, named

threshold, the content of the

corresponding

corresponding subset is rejected.

For every subset that overcomes

the threshold of certainty, the

process is repeated. Now, the

inputs to the FL engine are the

level 2 weights for the

corresponding index terms and

the process is repeated. The final

output corresponds to the

elements of the last level that is

to say, the objects whose degree

of certainty overcomes the

definitive threshold. Figure 4.1

shows the process in two level

hierarchic structures.

Implementing FL engine obtained

success to a great level.

Fuzzy Modeling for Panini's Sanskrit Grammar Indian languages have long

history in World Natural

languages. Panini was the first to

define Grammar for Sanskrit

language with about 4000 rules in

fifth century. These rules contain

uncertainty information. It is not

possible to Computer processing

of Sanskrit language with

uncertain information. Grammars

are defined to either programming

languages or natural languages.

Computer processing of natural

languages and language

translations is an application area

in the computer field. Indian

Languages

eld. I

languages are having long

history. Panini proposed

grammar with 4000 rules for

Sanskrit. These are categorized

into different sets. One of them

is Syadvada set. The Syadvada

set contains seven possibilities

they are given below.

1. May be, it is. (Syadasti)

µSyadasti(x) -> [0; 1]

2. May be, it is not (Syad nasti)

Syad nasti = 1 - µSyadasti(x)

3. May be it is, and it is not at

different times (Syadasti-nasti)

µSyadasti(x)^(1-µSyadasti(x) ^

µdifferenttimes(x; y))

4. May be it is and it is not at

the same time and is

indescribable

µSyadasti(x)^(1-µ)0 µSyadasti(x)

µdifferenttimes(x;t)) ^ µdifferenttimes(x)

5. May be it is and yet

indescribable.

(Syad astiavaktavya)

=µSyadasti(x) ^ µdifferenttimes(x)1/2

6. May be it is not, and also

indescribable (yad astinasti

avaktavya)

(1-µSyadasti(x)) ^ µdifferenttimes(x)

This fuzzy representation of the

Sanskrit sentences shall be

further used for fuzzy

reasoning. For instance,

consider two sentences

CLEAR Dec 2012 6

May be, it is. (Syadasti)

May be it is, and it is not at different

times (Syad asti-nasti)

The inference will be given as using R1

it is not at different times with the

fuzziness (Syadasti) ^ (Syad asti-

nasti).

Conclusion

Fuzzy logic deals mathematically with imprecise

information usually employed by humans. Fuzzy

Logic and fuzzy systems tries to mimic human

thinking and approximations. It is multi-valued

logic that extends Boolean logic.

Fuzzy Logic based NLP system can learn from a

linguistic corpus the fuzzy semantic relations

between the concepts represented by words and

use such relations to process the word

sequences generated by speech recognition

systems. An intelligent agent based on fuzzy

logic is used for information extraction. A new

term weighting scheme based on fuzzy logic is

introduced. When perceptions are described in

words, manipulation of perceptions is reduced to

computing with words (CW). FL is applied for

computation with words.

Panini proposed grammar with 4000 rules for

Sanskrit. Fuzzy logic and fuzzy reasoning are

discussed to deal with uncertainty

information in Panini's Sanskrit Grammar.

References:

1. Jiping Sun, Fakhari Karray, Otman Basir

& Mohamed Kamel ,‖Fuzzy Logic-Based

Natural Language Processing and Its

Application to Speech Recognition,"

Department of Electrical and Computer

Engineering, University of Waterloo.

2. Lot A. Zadeh ―From Computing with

Numbers to Computing with Words

from Manipulation of Measurements to

Manipulation of Perceptions," in Int. J.

Appl. Math. Comput. Sci., 2002, Vol.12,

No.3, 307324.

3. Timothy J Ross (2010), Fuzzy Logic with Engineering Applications. Third

Edition, Wiley India Pvt.Ltd.

4. Zadeh L. A., "Fuzzy sets," Inf. Control Vol. 8, pp. 338353.

5. P. Venkata Subba Reddy, ―Fuzzy

Modeling and Natural Language Processing for Paninis Sanskrit

Grammar‖, Journal of Computer Science and Engineering, Volume 1, Issue 1, May

2010.

6. Ropero, J., et al. ―A Fuzzy Logic intelligent agent for Information

Extraction: Introducing a new Fuzzy Logic-based term weighting scheme. “

Expert Systems with Applications (2011)doi:10.1016/j.eswa.2011.10.009

CLEAR Dec 2012 7

Indic Language Computing: A Review

Author

Manu Madhavan

M. Tech Computational Linguistics Govt. Engineering College,

Sreekrishnapuram Palakkad

Palakkad In this twenty first century, where

Computation and Information technologies

have reached uncomparable heights,

Language Computing may not be a buzz

word. It is the most evolving research

area, making the fast growing technologies

to fastest. The people involved and the

organizations invested in this area show

the future and scope of this technology.

Even though India is a dominant IT service

provider, the Language computing is still

struggling here to find its market place.

Why Indian engineers fail to bring the

technology to our common man? This

article collaborates different views on Indic

Language Computing, the challenges and

applications.

Through the current technological

innovations related IT movement, our

country is promoting the maximum

exploration of electronic media and

internet for reaching the people. But, in

many under-developed areas of the

Country, people only know their own

mother tongue for communication,

‗exploring‘ as visualized by

Government not effective. The

solution is providing the technology

in their own language. People

throughout the world have been

using computers and Internet in

their own languages. Somehow,

Indian users are compelled to use

them in English. In western

countries, the language computing

application is an active research

area. They developed many

intelligent systems for English, even

with speech capability. But with

almost three dozen major languages

and hundreds of dialects, the task is

more complex in India. The tools

present in the global market cannot

be replicated owing to the

complexity of multiple languages

that exist in the country. For

translation in Indian languages one-

to-one mapping of each word as it is

to form a sentence is not workable.

The methodology to be followed

here is to first process the source

language, convert words according

to the target language, and then

process it all again with respect to

the target language for the

conversion to make sense. With

these complexities, the current

translation systems and

other language computing

resources developed by

different research institutes

and volunteer NLP

enthusiasts, shows a hopeful

future.

Challenges:

Indian language computing

has faced many challenges

since the early ages of

language computing and

even today. Let‘s go through

some of the

challenges in Indic language

computing.

Dialects: Apart from the

typical nature of Indian

languages, cultures also

affect our language usage

and pronunciation. For

example, in northern parts

of India, Hindi is spoken in

varied forms across different

states and cities. Thus we

cannot have a generic tool,

CLEAR Dec 2012 8

especially for translation, and all tools

have to be developed for all of the

languages.

Corpus: One of the important

resource for language computing is

corpus. Some languages are spoken

by large number of people, others by

a small group. So, getting good

corpus is difficult. The criteria for

sample collection require the target

group to be computer savvy and

conversant in English as well as the

local language. This narrows down the

number of people who can be

contacted for giving sample of the

local lingo.

Linguistic Features: Indian

languages are morphologically richer

than English. So, computing all the

valid inflections and derivations in

language is challenging. A relief is

that the language is strictly structured

by well defined grammars, and the

ambiguity is less compared to English.

The presence of post fixes instead of

prefixes and existence of free word

order make the things more difficult.

Translating Jargons: Most of the

computer jargons and technical

phrases were not grammatically

complete sentences, they were just

computer commands. Also, words like

document, folder, delimiters, add-ons

are not enlisted in any dictionary of

Indian languages. While in some

languages it has been transliterated

and retained as it is, experts of some

other languages went on to create a

whole new set of words

corresponding to the IT terminology.

Script: Representing the Indian

scripts in digital format is difficult,

even with the development of

Unicode. The lack of standards in this

representation suppresses the use of

local languages in internet media.

ISCII representation similar to ASCII

for English is a standard developed

for Indian languages. Now

Government of India accepted

Unicode standard characters for

Indian languages. Transliteration for

Indian languages is considerably

successful today. Indic languages are

languishing due to lack of

standardization and available

technology.

Shakti Standard Format Shakti Standard Format (SSF)

is a highly readable

representation for storing

language analysis. It is

designed to be used as a

common format or common

representation on which all

modules of a system operate.

The representation is

extensible in which different

modules add their analysis.

SSF also permits partial

analysis to be represented and

operated upon by different

modules. This leads to graceful

degradation in case some

modules fail to properly

analyze a difficult sentence.

(Developed by LTRC, IIIT-

Hyderabad)

…When the user

dials the Voice

Number of a

website, he or she

gets to hear the

content of the

respective site over

the phone) is an

interesting

application ….

CLEAR Dec 2012 9

Applications:

One prominent use is the

digitization or creation of e-

books of the mounds of rich

literature in different Indian

languages. This would help

greater and better digitization of

libraries across the Indian

cultural terrain---- Physical

documents can be converted

into e-documents and these can

be further read out using text-

to-speech engines developed by

private companies and

institutions.

Another application of language

computing comes to play with

the concept of cross-lingual

search and the wordnet that

are being developed by Pushpak

Bhattacharyya, professor of

computer science engineering at

IIT-Bombay and head of

Laboratory for Intelligent

Internet Access at the institute.

Software localization TDIL

defines Software localization as

the process of adapting a software product

to the linguistic, cultural and technical

requirements of a target market. This

process is labor-intensive and often

requires a significant amount of time from

the development teams. So in addition to

translation, the localization process may

also include adapting graphics to the target

markets, modifying content layout to fit the

translated text, converting to local

currencies, using of proper formats for

dates, addresses, and phone numbers,

addressing local regulations and more. The

goal is to provide a product with the look

and feel of having been created for the

target market to eliminate or minimize

local sensitivities.

Speech is the area yet to be explored.

There are hardly any successful speech

processors. With an efficient speech system

in local language (say) for railway ticket

booking, helps the illiterate people. The

IBM voice web (When the user dials the

Voice Number of a website, he or she gets

to hear the content of the respective site

over the phone) is an interesting

application in this field. A language tutor

for Indian languages can also possible from

speech realm. Mobile applications, based

on NLP and speech systems have

interesting scope in Indian market.

Microsoft’s Bhashaindia

Towards establishing a direct

contact and providing a

common platform to the larger

community of people, including

students, linguists, and

academicians etc, Microsoft

launched the portal

"www.bhashaindia.com". This

portal aims at building a

community of developers and

linguistic academia who will

contribute towards the

development and use of Indian

languages for PC usage. The

portals a one-point reference

for all Indic related activities.

Additionally this portal would

be of interest and use for

general PC users, educational

and training institutions, and

government agencies.

BhashaIndia, India‘s leading

Indic computing community

portal has over 15000

registered users and continues

to grow by the day. It has

become a one stop center for

all resources related to Indian

language computing. Articles,

latest news, snippets of

interesting information and

resources like applications

related to Indic computing are

all available on this site. Today

BhashaIndia has become the

destination for anybody

interested in Indian language

computing.

Ref : www.bhashaindia.com

-Sreeejith C

CLEAR Dec 2012 10

Research Initiatives:

Different centers of C-DAC—in

Bangalore, Kolkata, Mumbai,

Noida, Pune, and

Thiruvananthapuram—work on

language computing technologies.

Their activities include

development of smaller utilities

like desktops and Internet access

in Indian languages and core

research in areas of machine

translation, OCR, cross-lingual

access, search engines,

standardization, digital library,

and more. Other smaller groups

are also being seen as key players

in the field -- including the IIT-

Madras group that has been

working and incubating innovative

Indian-language solutions, the

NCST (National Centre for

Software Technology) in Mumbai,

and the IIIT (International

Institute of Information

Technology) in Hyderabad, which

has done impressive work on

machine-translation and related

areas. The works of NLP

communities like Swathanthra

Malayalam Computing(SMC),

International Forum for

Information Technology in

Tamil(INFIT), wikimedia etc are

well appreciable and have key role

in development of this area.

SILPA project, Linux Indian

language versions are some of

the efforts from these volunteer

groups. The open source

environment provides a large

scope future development.

Need for Tomorrow:

The major problem in this field is

the lack of central co-ordinations.

More people have to come

forward to work in this area.

Government has to take

necessary steps to teach

language computing technology

for engineering graduates. It is

very clear that the survival of

language in the cyber world is

essential to make the citizen a

global man.

CLIP

The Microsoft Captions

Language Interface Pack (CLIP)

is a simple language translation

solution that uses tooltip

captions to display results. Use

CLIP as a language aid, to see

translations in your own dialect,

update results in your own

native tongue or use it as a

learning tool.

CLIP is designed to enable and

support indigenous languages

and native dialects and is the

result of the close collaboration

between Microsoft and local

communities. Users will be able

to download multiple languages,

switching target translations

quickly and easily.

To use, simply move your

mouse around the screen and

halt briefly over any text you

want translated. Users can also

add their own translations and

copy and paste any results.

- Sreejith C

References:

1.http://magazine.itmagz.com/ind

ex.php/component/content/article/

521.html

2.http://bhashaindia.com/Develop

ers/Tutorial/Pages/IndianLanguage

Computing.aspx

3.http://www.technologyreview.in/

computing/37921/

CLEAR Dec 2012 11

Natural Language Processing and Human Computer Interaction

Author

Sreejith C

M. Tech Computational Linguistics Govt. Engineering College,

Sreekrishnapuram Palakkad

Palakkad

Natural Language Processing, also

abbreviated as NLP, is a field of

computer science. The field focuses on

helping computers understand and

interpret human languages. Human

languages are also known as natural

languages, thus the term

NLP. Computers are programmed to

try and interpret an input sentence in a

natural language into a more formal

computerized representation. Many

NLP problems apply to both generation

and understanding natural

languages. A computer must be able

to understand the model of a natural

language in order to understand

it. Patterns in natural languages must

be programmed in order to produce a

grammatically correct sentence in that

particular natural language.

NLP is considered to have great

potential to provide services for

corporate companies and governmental

agencies. In present times, electronics

are relied upon for many day-to-day

tasks and our society relies on

electronics more than it did the

previous day. This high demand on

sophisticated electronics shows a need

for technology such as NLP.

What makes this field

really interesting is

that not only do we

have the computer try

and understand a

human language; we

have a way to

investigate and learn

more about natural

languages in

general. To illustrate

this, we can look at

data mining, a field

that tries to describe

and predict

outcomes. With data

mining, Wal-Mart was

able to figure out that

diapers and beer were

bought together. This

allowed them to

position those two

groceries closer

together. We can see

that a normal human

would not be able to

figure out this relation

but with a computer,

it is very possible to

find out more

information about

natural languages

using NLP.

Over the past few years, our

research group has been

comprised of researchers from

both the Human-Computer

Interaction (HCI) and the Natural

Language Processing (NLP)

communities, and they have

thus been exploring how the two

communities can benefit each

other. This paper intends to

present several views on this

topic, as well as some basic

concepts and examples of how

the two disciplines meet in

specific projects. This paper will

focus on the relationships that

can exist between HCI and NLP.

CLEAR Dec 2012 12

information about natural languages

using NLP.

Some neat technologies have been

developed to explore the field of

NLP. For example, there are chat

bots that can have conversations with

a human or another bot. These

machines can learn more about how

humans talk to each other and

simulate a human. Other applications

include tools to help investigate

plagiarism...so for example, if I

decided to simply copy and paste

content from a small set of websites,

programs can figure out that there is

a high relationship between the page I

created and the websites that were

listed as a reference. This compilation

of websites explores NLP by exploring

its history, its uses, and its side

effects, good and bad.

Human–computer interaction Human–computer Interaction (HCI)

involves the study, planning, and

design of the interaction between

people (users) and computers. It is

often regarded as the intersection of

computer science, behavioral

sciences, design and several other

fields of study. Because human–

computer interaction studies a human

and a machine in conjunction, it

draws from supporting knowledge on

both the machine and the human side.

On the machine side, techniques in

computer graphics, operating

systems, programming languages,

and development environments are

relevant. On the human side,

communication theory, graphic and

industrial design disciplines,

linguistics, social sciences, cognitive

psychology, and human factors such

as computer user satisfaction are

relevant. Engineering and design

methods are also relevant. Due to the

multidisciplinary nature of HCI, people

with different backgrounds contribute

to its success. HCI is also sometimes

referred to as man–machine

interaction (MMI) or computer–human

interaction (CHI). A basic goal of HCI

is to improve the interactions between

users and computers by making

computers more usable and receptive

to the user's needs. Researchers in

HCI are interested in developing new

design methodologies, experimenting

with new hardware devices,

prototyping new software systems,

exploring new paradigms for

interaction, and developing models

and theories of interaction.

Relationship between

HCI and Natural

Language Processing

To answer the first hot

question: Are HCI and NLP

complementary fields? For that

We need to clarify our

understanding of goals and

methods of both disciplines.

Indeed, it seems to us that the

gap can only partially be

explained by epistemic

distinctions, but that it is

related to strong discipline

boundaries separating HCI

from AI.

Of course, HCI and

NLP should meet in

one obvious place:

the natural language

interface. Natural

language interfaces

have several

advantages over

direct manipulation

CLEAR Dec 2012 13

As a matter of fact, HCI and NLP

attempt to reach a common goal:

simplifying user interaction with

information systems. Despite this,

historically they have followed two

antithetic design approaches. HCI

is, by definition, user-centered;

NLP has for long been based on a

prevailing system-centered view.

HCI concentrates on interfaces,

artificial modules able to translate

digital signals into analog

representations. The focus of

attention has always been on

users: interfaces adapt computers

to limits, capabilities and needs of

humans. The focus of attention

has always been on users:

interfaces adapt computers to

limits, capabilities and needs of

humans. On the other hand, for

many years NLP has focused on

systems, attempting to reproduce

verbal communication at the

human-computer interface by

architectures processing

conversational inputs. In a perfect

NL system the traditional concept

of user-interface tends to

disappear: the language itself

constitutes the interface.

Yet, both HCI and NLG are concerned

with the effectiveness of

communication, and we can see

parallels between their various

concerns. HCI design practitioners are

concerned with such issues as

information grouping and

differentiation, consistency with the

ways users perform their tasks, and

clear specification of the purpose of

each interface element. This is

analogous to ensuring in NLG that a

chunk of text is coherent and achieves

one or more specific communicative

goals the user can recognize, and that

a sequence of such chunks (or moves

in a dialogue) is also coherent. Of

course, HCI and NLP should meet in

one obvious place: the natural

language interface. The main

paradigm in HCI design today is direct

manipulation. However, natural

language interfaces have several

advantages over direct manipulation:

they allow references to objects that

are not directly visible and to events

that have occurred in the past or will

occur in the future. In addition, with

the increasing number of small

displays (e.g., mobile phones) and

mobile devices, vocal interaction

between user and on-line services will

probably become more

prominent. This is an obvious

instance where NLG and HCI

experts should collaborate.

Speech interfaces are not the

only point of contact between

HCI and NLG, though. Another

type of interface where the two

disciplines meet is one in

which documents act as

interface. This is the case, for

example, for web pages, or

any form of hypertext. There,

interaction occurs within the

document/text. While issues

related to language and

dialogue are important here,

so are other interactional

issues. An example of these

issues is the trade-off between

the number of hypertext links

the user must traverse to

arrive at the appropriate

information and the amount of

text to be presented at each

point. Another example

concerns the positioning of

new windows and whether the

old window disappears or not.

A third example concerns the

way a hypertext anchor is

specified, and if and how

CLEAR Dec 2012 14

information about the target page

should be provided. These issues

relate to the interface proper, and

the interaction between the user

and the computer.

A Look into the Future: How NL Could Change HCI

One goal for artificial intelligence

work in natural language is to

enable communication between

people and computers without

resorting to memorization of

complex commands and

procedures. Automatic translation—

enabling scientists, business people

and just plain folks to interact easily

with people around the world—is

another goal. So, research will

continue to enable humans to

communicate more naturally with

their computers, with the ultimate

goal being to determine a system of

symbols, relations, and conceptual

information that can be used by

computer logic to implement

artificial language interpretation.NLP

has continuing implications for

translation, gaming, summarization,

question answering, information

retrieval, and robot creation.

Information management and data

querying would benefit hugely from

NLP.NLP can help with extracting

and structuring text-based clinical

information, making clinical data

readily accessible in human

language form.

As NL will gain more importance in

HCI, interaction will be less and less

a matter of pushing buttons and

dragging slides, and more and more

a matter of specifying operations

and assessing their effects through

the use of language. Computers will

no longer be medium where

performing tasks fully requires

users to define and execute all the

actions; computers will work at a

higher level, being able to split

actions in tasks and autonomously

executing them. The change can

deeply affect the paradigm of

interaction: from doing to having it

done consequently, the mental

representation elicited by computers

may drastically evolve.

Real Life Examples

1. Chatter bots or Artificial Conversational Entities

A type of computer program that

simulates a real conversation via

auditory or textual methods; most

simply scan for keywords within

input from human conversation and

create a reply using matching

keywords from an available

database. They ―converse‖ by

recognizing cue words or phrases

from the human user, which allows

them to use pre-prepared or pre-

calculated responses which can

move the conversation on in an

apparently meaningful way

without requiring them to know

what they are talking about.

For example, if a human types, "I

am feeling very worried lately,"

the chatterbox may be

programmed to recognize the

phrase "I am" and respond by

replacing it with "Why are you"

plus a question mark at the end,

giving the answer, "Why are you

feeling very worried lately?"

A similar approach using

keywords would be for the

program to answer any comment

including (Name of celebrity)

with "I think they're great, don't

you?" Humans, especially those

unfamiliar with chatter bots,

sometimes find the resulting

conversations engaging. Critics

aren‘t impressed.

CLEAR Dec 2012 15

2. Robot Nurse

Robot-Nurse, developed by

Samsung and Robot-Hosting.com

is a very practical application of

NLP. The machine uses face

recognition (via camera), as well

as voice recognition (via

microphone) and has flexible arms

and grasping tools for "hands," the

better to perform the more menial

tasks usually done by nursing

staff. Researchers at the University

of Auckland are creating the

knowledge base for the robot.

Using several global server clusters

as a brain, Robot-Nurse will tend

to patients when nurses sleep at

night. "She" can reason logically,

deliver prescriptions, and remind

patients of things like a daily

exercise routine, by acting as a

coach and encouraging them

verbally.

Another way Robot-Nurse bonds

with her patients is to keep those

company who have no visitors to

tell them jokes or simply talk with

them.

Robot-Nurse is too short to change

bedpans, but perhaps the later

versions will be able to free their

human counterparts from this

unpleasant chore.

3. The Isolde Project

The Isolde project is concerned

with the design and development

of a tool to support the production

of hypertext-based on-line help for

software systems, using language

technology (Paris et al., 1998).The

projects emphasis was to try to

address some of the limitations of

current language technology that

prevent its use in realistic settings.

In particular, our concern was with

the knowledge acquisition issue:

how to obtain the knowledge

required for the generation of on-

line help.

4. Stair, the Stanford Robot

University of Stanford is building a

robot that can navigate home and

office environments, pick up and

interact with objects and tools,

and intelligently converse with and

help people in these environments

Over the long term, Stair‘s

creators envision a single robot

that can perform tasks such as:

Fetch or deliver items around

the home or office

Tidy up a room including

picking up and throwing

away trash

Prepare meals using a

normal kitchen

Use tools to assemble a

bookshelf

Conclusion

In conclusion, from a

methodological point of view,

both HCI and NLP need to

upgrade their scientific apparatus

to cope with the design of social

artifacts. It is well clear that the

HCI and NLP communities should

work together on a wide variety

of problems. There are several

areas where the cross-fertilization

can occur, and the combination of

the two types of expertise could

be beneficial. Hence there is still

enough to research and Improve

in this area, a promising future is

waiting in this field.

CLEAR Dec 2012 16

References:

1. http://www.cngl.ie/drupal/sites/default/files/papers

2/p4333-karamanis.pdf

2. Antonella De Angeli and Daniela Petrelli, ―

Bridging the gap between NLP and HCI: A new

synergy in the name of the user” Cognitive

Technology Laboratory Department of Psychology -

University of Trieste Via S. Anastasio , 12 ; I-34100,

Trieste, Italy

3. Cile Paris and Nadine Ozkan ― Motivating the

cross-fertilization between HCI and Natural

Language Processing “, CSIRO/MIS Locked bag 17,

North Ryde NSW 1670, Australia.

4. https://sites.google.com/site/naturallanguageproce

ssingnlp/Home/real-life-examples

5. http://www.cnlp.org/cnlp.asp?m=5&sm=0

6. http://www.cs.utep.edu/novick/nlchi/papers/Paris.

htm

7. De Angeli* and Daniela Petrelli ―Bridging the gap

between NLP and HCI: A new synergy in the

name of the user‖,

8. Do HCI and NLP Interact? CHI 2009 ~ Spotlight on

Works in Progress ~ Session 2 April 4-9, 2009 ~

Boston, MA, USA

Inviting Article for

CLEAR March 2013

We are inviting thought-provoking articles,

interesting dialogues and healthy debates on

multifaceted aspects of Computational

Linguistics, for the forthcoming issue of CLEAR

(Computational Linguistics in Engineering And

Research) magazine, publishing on March 2013.

The topics of the articles would preferably be

related to the areas of Natural Language

Processing, Computational Linguistics and

Information Retrieval. The articles may be sent

to the Editor on or before 15th February, 2013

through the email simplequest.in@gmail.com.

-Editor

CLEAR Dec 2012 17

Google Driverless Car

Author

Robert Jesuraj K

M. Tech Computational Linguistics Govt. Engineering College,

Sreekrishnapuram Palakkad

Palakkad The Google driverless car is a project

by Google that involves developing

technology for driverless cars. The

project is currently being led by Google

engineer Sebastian Thrun, director of

the Stanford Artificial Intelligence

Laboratory and co-inventor of Google

Street View. Thrun's team at Stanford

created the robotic vehicle Stanley

which won the 2005 DARPA Grand

Challenge and its US$2 million prize

from the United States Department of

Defense. The team developing the

system consisted of 15 engineers

working for Google, including Chris

Urmson, Mike Montemerlo, and

Anthony Levandowski who had worked

on the DARPA Grand and Urban

Challenges.

The U.S. state of Nevada passed a law

on June 29th, 2011 permitting the

operation of driverless cars in Nevada

and California. Google had been

lobbying for driverless car laws. The

Nevada law went into effect on March

1, 2012, and the Nevada

Department of Motor Vehicles

issued the first license for a self-

driven car in May 2012. The license

was issued to a Toyota Prius

modified with Google's

experimental driverless technology.

While Google had no immediate

plans to commercially develop the

system, the company hopes to

develop a business which would

market the system and the data

behind it to automobile

manufacturers. An attorney for the

become a reality because

"the technology is now

advancing so quickly that it

is in danger of outstripping

existing law, some of which

dates back to the era of

horse-drawn carriages".

Google lobbied for two bills

that made Nevada the first

state where driverless

vehicles can be legally

operated on public roads.

The first bill is an

California Department of Motor

Vehicles raised concerns that "The

technology is ahead of the law in

many areas," citing state laws that

"all presume to have a human

being operating the vehicle". to the

New York Times, policy makers and

have argued that new laws be

required if driverless vehicles are to

amendment to an electric

vehicle bill that provides for

the licensing and testing of

autonomous vehicles. The

second bill will provide an

exemption from the ban on

distracted driving to permit

occupants to send text

messages while sitting

behind the wheel.

CLEAR Dec 2012 18

a human driver to take control

by stepping on the brake or

turning the wheel.

Google's driverless test cars

have about $150,000 in

equipment including a $70,000

lidar (laser radar) system. The

range finder mounted on the top

is a Velodyne 64-beam laser.

The Google car project team was

working in secret in plain view

on vehicles that can drive

themselves, using artificial-

intelligence software that can

sense anything near the car and

mimic the decisions made by a

human driver. With someone

behind the wheel to take control

if something goes awry and a

The two bills came to a vote before the

Nevada state legislature‘s session ended

in June 2011. It has been speculated

that Nevada was selected due to the Las

Vegas Auto Show and the Consumer

Electronics Show, and the high likelihood

that Google will present the first

commercially viable product at either or

both of these events. Google executives,

however, refused to state the precise

reason they chose Nevada to be the

maiden state for the driverless car.

Nevada passed a law in June 2011

concerning the operation of driverless

cars in Nevada, which went into effect

on March 1, 2012. A Toyota Prius

modified with Google's experimental

driverless technology was licensed by

the Nevada Department of Motor

Vehicles (DMV) in May 2012. This was

the first license issue in the United

States for a self-driven car. License

plates issued in Nevada for autonomous

cars will have a red background and

feature an infinity symbol (∞) on the left

side because, according to the DMV

Director, "...using the infinity symbol

was the best way to represent the 'car

of the future'." Nevada's regulations

require a person behind the wheel and

one in the passenger‘s seat during tests.

Google's autonomous system permits

technician in the passenger

seat to monitor the navigation

system, seven test cars have

driven 1,000 miles without

human intervention and more

than 140,000 miles with only

occasional human control.

One even drove itself down

Lombard Street in San

Francisco, one of the steepest

and curviest streets in the

nation. The only accident,

engineers said, was when one

Google car was rear-ended

while stopped at a traffic light.

Autonomous cars are years

from mass production, but

technologists who have long

dreamed of them believe that

they can transform society as

profoundly as the Internet

has.

CLEAR Dec 2012 19

Robot drivers react faster than

humans, have 360-degree perception

and do not get distracted, sleepy or

intoxicated, the engineers argue. They

speak in terms of lives saved and

injuries avoided — more than 37,000

people died in car accidents in the

United States in 2008. The engineers

say the technology could double the

capacity of roads by allowing cars to

drive more safely while closer together.

Because the robot cars would

eventually be less likely to crash, they

could be built lighter, reducing fuel

consumption. But of course, to be truly

safer, the cars must be far more

reliable than, say, today‘s personal

computers, which crash on occasion

and are frequently infected.

The Google research program using

artificial intelligence to revolutionize

the automobile is proof that the

company‘s ambitions reach beyond the

search engine business. The program is

also a departure from the mainstream

of innovation in Silicon Valley, which

has veered toward social networks and

Hollywood-style digital media.

During a half-hour drive beginning on

Google‘s campus 35 miles south of San

Francisco, a Prius equipped with a

variety of sensors and following a route

programmed into the GPS navigation

system nimbly accelerated in the entrance

lane and merged into fast-moving traffic

on Highway 101, the freeway through

Silicon Valley.

It drove at the speed limit, which it knew

because the limit for every road is included

in its database, and left the freeway

several exits later. The device atop the car

produced a detailed map of the

environment.

The car then drove in city traffic through

Mountain View, stopping for lights and

stop signs, as well as making

announcements like ―approaching a

crosswalk‖ (to warn the human at the

wheel) or ―turn ahead‖ in a pleasant

female voice. This same pleasant voice

would, engineers said, alert the driver if a

master control system detected anything

amiss with the various sensors.

The car can be programmed for different

driving personalities — from cautious, in

which it is more likely to yield to another

car, to aggressive,

where it is more

likely to go first.

Christopher Urmson,

a Carnegie Mellon

University robotics

scientist, was behind

the wheel but not

using it. To gain

control of the car he

has to do one of

three things: hit a

red button near his

right hand, touch the

brake or turn the

steering wheel. He

did so twice, once

when a bicyclist ran

a red light and again

when a car in front

stopped and began

to back into a

parking space. But

the car seemed likely

to have prevented an

accident itself.

"...using the infinity symbol was the best way

to represent the 'car of the future'."

CLEAR Dec 2012 20

When he returned to automated ―cruise‖

mode, the car gave a little ―whir‖ meant

to evoke going into warp drive on ―Star

Trek,‖ and Dr. Urmson was able to rest

his hands by his sides or gesticulate

when talking to a passenger in the back

seat. He said the cars did attract

attention, but people seem to think they

are just the next generation of the Street

View cars that Google uses to take

photographs and collect data for its

maps.

The project is the brainchild of Sebastian Thrun, the 43-year-old director of the Stanford Artificial Intelligence

Laboratory, a Google engineer and the co-inventor of the Street View mapping service.

Besides the team of 15 engineers working on the current project, Google hired more than a dozen people, each with a

spotless driving record, to sit in the driver‘s seat, paying $15 an hour or more. Google is using six Priuses and an Audi

TT in the project.

The Google researchers said the company did not yet have a clear plan to create a business from the experiments. Dr.

Thrun is known as a passionate promoter of the potential to use robotic vehicles to make highways safer and lower the

nation‘s energy costs. It is a commitment shared by Larry Page, Google‘s co-founder, according to several people

familiar with the project.

CLEAR Dec 2012 21

Author

Razee Marikar

Subex Azure Limited, Bangalore

GNU Octave

Octave is a tool for numerical calculations and solving

numerical problems. It also has graphing and

visualization capabilities. It can be either used in an

interactive way, or by writing non-interactive

programs. In this article, I give an overview of the

basic capabilities of Octave.

Installing and Running Octave

If you are on a Linux environment, check the package

manager of the OS. You should find octave as one of

the packages. Check the download page at

http://www.gnu.org/software/octave/ for obtaining

Octave for other operating systems or to build from

source. Now you can run it.

On Linux, open a command shell (on the GUI if you

want to use it to view graphs), and type 'octave'. On

Windows, depending on your installation method, you

may need to open your cygwin environment and run

octave or open it from start menu.

Simple Calculations

Let's get started with simple calculations. Suppose you

want to find out the result of a simple calculation like

(2+10i)x(3π+5i)³. On the octave prompt, you should

enter the command as follows, using syntax similar to

most other languages. But remember, there are some

differences, for example, octave can handle irrational

numbers:

(2 + 10i) * (3*pi + 5i) ^3

Octave interprets "i" to identify the irrational part,

it understands constants like pi, and it interprets

"^" as the power function.

You can also store results to a variable. Here are

some examples:

Octave-3.2.4.exe:12> a=10

a = 10

Octave-3.2.4.exe:13> a*a

ans = 100

Octave-3.2.4.exe:14> b=(3 + 5i) +

(2.2 + 3.1i) * (10 + 2i)

b = 18.800 + 40.400i

Octave-3.2.4.exe:15> c = a*b

c = 188 + 404i

Octave-3.2.4.exe:17> c = a*b+42;

Octave-3.2.4.exe:18> c

c = 230 + 404i

One thing to be noted here is that if you enter a

semi column at the end of the command, the result

of the operation won't be printed. It is useful while

using Octave in non-interactive mode using a

program stored in a file.

Matrix Calculations

Octave is very good at handling matrices. In this

article, I will quickly introduce you on how to work

with matrices on Octave. First, to enter and store a

matrix into variables:

Octave-3.2.4.exe:19> A = [1 2 3; 5

7 2; 7 8 0];

Octave-3.2.4.exe:20> B = [5 7 5; 1

0 1; -1 3 5];

Octave-3.2.4.exe:21> A

A =

1 2 3

5 7 2

7 8 0

CLEAR Dec 2012 22

Octave-3.2.4.exe:22> inv(A)

ans =

1.06667 -1.60000 1.13333

-0.93333 1.40000 -0.86667

0.60000 -0.40000 0.20000

Octave-3.2.4.exe:23> A + B

ans =

6 9 8

6 7 3

6 11 5

Octave-3.2.4.exe:24> A * B

ans =

4 16 22

30 41 42

43 49 43

Octave-3.2.4.exe:25> 2*A

ans =

2 4 6

10 14 4

14 16 0

Octave-3.2.4.exe:26> B/A

ans =

1.80000 -0.20000 0.60000

1.66667 -2.00000 1.33333

-0.86667 3.80000 -2.73333

Using the above examples, it should be evident how this

can be used to solve numeric equations.

References and further reading:

1. Official documentation here:

http://www.gnu.org/software/octave/doc/interpreter

2. Introduction to Octave by Dr. P.J.G. Long based

on the Tutorial Guide to Matlab written by Dr. Paul

Smith:

http://wwwmdp.eng.cam.ac.uk/web/CD/engapps/oct

ave/octavetut.pdf

3. Machine Learning classes available online by

Stanford University (Prof. Andrew Ng)

CLEAR Dec 2012 23

Hello World,

Let me share my experience, from the valedictory function of Amrita CLMT

workshop. During a discussion on Indian Language Computing, one delegate from

Andhra commented that Indians are reluctant to use their language in digital world.

His observation has relevance in the light of past, present and future scenario in ILC.

The people interested in this area are few. Many technologist working in IT sectors

have not even heard of this area. Even though India is one among in top IT

solutions, technology is away from most of the citizens.

Why ILC fails to reach the common man‘s desktop? What make language computing

so much difficult?

The answer is simple: "This is not a rocket science. Solutions are possible‖. We need

linguists interested in technology and technocrats interested in language.

Government has to take necessary steps to include language technology for

engineering graduate. Moreover, people should have an enthusiasm on their

language, not to divide themselves, but to join the global technology.

Few months before, Sam Pitroda -- technical advisor to the Prime Minister of India --

told that, ―India needs lot of language technologists in near future‖. This shows the

scope and growth of language technology. We are not bothering about people‘s

attitude. We have a bright future.

Thanks for your ‗ ‘ and ‗ ‘, you put for the last issue of CLEAR. This motivated

Simple Groups to bring the second issue.

Expecting your future supports!

Wish you all the best....

Manu Madhavan

L

A

S

T

W

O

R

D

CLEAR Dec 2012