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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….

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

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 …

CLEAR Dec 2012

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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


Fuzzy Logic Applications in Natural Language Processing Fuzzy

Author

Divya S M. Tech Computational Linguistics Govt. Engineering College, Sreekrishnapuram Palakkad

Palakkad

Fuzzy

Logic

has

widespread applications in

logic is an approach to

based term weighting scheme

computing based on degrees

used for information extraction.

of truth rather than the usual

We also discuss how fuzzy logic

true or false (1 or 0) Boolean

and fuzzy reasoning are used to

logic on which the modern

deal with uncertainty

computer

is

information in Panini's Sanskrit

language

(like

based.

Natural

most

other

activities in life) is not easily

Grammar.

Fuzzy Logic

translated into the absolute terms of 0 and 1. Fuzzy logic

Our

includes 0 and 1 as extreme

physical

language processing. Here

cases

largely

we also discuss a fuzzy

includes the various states of

reasoning.

truth in between. Fuzzy logic

(when compared to the precise

deals

quantities

the

field

logic

of

natural

based

language system

natural processing

for

speech

recognition, and a fuzzy

of

truth

but

mathematically

also

with

Fuzzy Logic has widespread in

the

field

of

We discuss some applications of fuzzy logic in NLP. Lotfi A Zadeh's work on Computing

logic and fuzzy reasoning

with Words is an important

are

with

application of fuzzy logic in

uncertainty information in

natural language processing.

used

Panini's Grammar.

to

deal

Sanskrit

Here we also discuss a fuzzy logic based natural language processing system for speech recognition, and a fuzzy logic

This

human

imprecision

required

by

is

nonetheless

a

quite useful to humans. The ability to embed such reasoning in

hitherto

complex

intractable

problems

and

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

freeway,

CLEAR Dec 2012

imprecise

based

form of information that can be

natural language processing.

also discuss how fuzzy

on

is

employed by humans.

scheme

information extraction. We

processes

most

computers)

applications

for

of

imprecise information usually

logic based term weighting used

understanding

others

washing

controlling

traffic

intersections, judging contests and a

on

a

clothes, at beauty

1


preliminary

understanding

complex

system.

problems

Fuzzy

And

for

logic

a

consequence related to the height of a

Fuzzy Set and Crisp Set

such

tall man, then the consequence can be

The universe of discourse is

the

applied or inferred in relation to his

the universe of all available

degree of membership in the tall set.

information

Basically,

a

problem. Once this universe

multivalve logic that allows intermediate

is defined it is able to define

values

certain

of

takes

focus.

Fuzzy

logic

resembles

human

decision making with an ability to generate

precise

solutions

certain or approximate It

fills

an

information.

important

engineering

design

vacant

purely

from

gap

methods

in

Fuzzy

to

approaches design),

(e.g.

and

be

(FL)

defined

is

between

purely

given

on

this

information space. Sets are

yes/no,

described

high/low,

rather

tall

or

etc. very

Notions fast

like

can

as

mathematical

be

abstractions of these events

and

and of the universe itself. A

processed by computers, in order to

classical set is defined by

apply

of

crisp boundaries, i.e., there

of

is

left mathematically

mathematical linear

events

a

conventional evaluations like true/false,

formulated by

Logic

on

control a

more

human-like

way

logic-based thinking

in

the

programming

approaches (e.g. expert systems) in

no

uncertainty

in

the

computers.

prescription or location of the

Fuzzy Logic can be used to generate

boundaries of

Fuzzy Logic allows something to be

solutions to problems based on "vague,

shown in Fig. 3.1a where the

partially true and partially false. A

ambiguous, qualitative, incomplete or

boundary of crisp set A is an

simple example follows: Is a man

imprecise information. The use of fuzzy

unambiguous line. In figure

who stands 170 centimeters (5‘6")

logic

in

3.1a, point a is clearly a

considered to be tall? Traditionally

natural language analysis compared to

member of crisp set A; point

we must define a threshold over

statistical and other approaches. It is

b is unambiguously not a

which a man of a certain height is

commonly

member of set A.

considered a member of the tall set

phenomena in natural language lend

and under which he is not. Fuzzy

themselves to descriptions by

Logic allows one to speak of a 170

mathematics, including fuzzy sets, fuzzy

cm man as both a member of the

relations and fuzzy logic.

tall set and the medium set, and

Fuzzy logic deals mathematically with

possibly even the short set. He may

imprecise information usually employed

be considered to a larger degree a

by humans. When considering the use

member of the medium set than he

of fuzzy logic for a given problem, an

is of the tall set. A man who stands

engineer or scientist should ponder the

190 centimeters will be to a higher

need for exploiting the tolerance for

degree a member of the tall set. If a

imprecision.

system design.

problem suggests there

is

an

effective

recognized

alternative

that

many

the

set, as

fuzzy

Figure 1(a): Crisp Set (b) Fuzzy set A fuzzy set, on the other hand, is prescribed by vague or ambiguous properties;

is some

CLEAR Dec 2012 consequence related to the height of a tall man,

2


Fuzzy Set A is represented as A. The

In

shaded

transition

boundary

represents

the

classical, for

or

crisp,

an

element

the

Underlying

in

the

capability

membership

this is

the

remarkable brains crucial

boundary region of A. In the central

universe

and

ability to manipulate perceptions

(unshaded) region of the fuzzy set,

non-membership in a given set is

of distance, size, weight, color,

point a is clearly a full member of

abrupt

speed,

the set. Outside the boundary region

element in a universe that contains

number,

of the fuzzy set, point b is clearly not

fuzzy

be

other characteristics of physical

a member of the fuzzy set. However,

gradual. This transition among various

and mental objects. Manipulation

the membership of point c, which is

degrees

be

of perceptions plays a key role in

on

is

thought of as conforming to the fact

human recognition, decision and

ambiguous. If complete membership

that the boundaries of the fuzzy sets

execution processes. Computing

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

are vague and ambiguous. Hence,

with words provides a foundation

is represented by value 1, and non-

membership of an element from the

for

membership in a set (such as point b

universe in this set is measured by a

perceptions a theory which may

in Fig. 3.1b) is represented by 0,

function that attempts to describe

have an important bearing on

then point c in Fig.3.1b must have

vagueness

how humans make and machines

some

of

element in the universe, say x, is a

might

membership (partial membership in

member of fuzzy set A then this

rational

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

mapping is given by ÂľA (x) Îľ [0,1].

environment

the

boundary

region,

intermediate

value

Presumably the membership of point c in A approaches a value of 1 as it moves

closer

to

the

central

between

sets

and

sets,

well

this

of

defined.

transition

membership

and

For

can

can

ambiguity.

If

an

an

A Computing

with

words,

is

a

membership

computation

c

in

A

are

words

and

approaches a value of 0 as it moves

propositions drawn from a natural

closer to leaving the boundary region

language,

of A. Fuzzy sets cover virtually all of

heavy, not very likely, Berkeley is

the definitions, precepts, and axioms

near San Francisco, etc. Computing

that define classical sets. Crisp sets

with

e.g.,

words

is

small,

large,

inspired

by

far,

the

are a special form of fuzzy sets; they

remarkable

are sets without ambiguity in their

perform a wide variety of physical and

membership (i.e., they are sets with

mental

unambiguous boundaries).

measurements and any computations.

CLEAR Dec 2012

truth,

likelihood

computational

make

force, and

theory

of

perception-based

decisions of

in

an

imprecision,

uncertainty and partial truth.

methodology in which the objects of

point

direction,

Fuzzy Logic and NLP

(unshaded) region of A, and the of

a

time,

human

tasks

capability

without

to

basic

perceptions

difference and

between

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).

any

3


In CTP, words play the role of labels of perceptions and, more generally,

perceptions

expressed

as

natural

propositions

language.

techniques

are

are

in

a

CW-based

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

functions

for

search

keys

fuzzy

membership

certain [Medina,

database 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

Speech recognition system is applied on restricted domains.

Fuzzy Logic has many applications in Natural Language Processing. Fuzzy

and

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

This means the vocabulary size senses

and

syntactic

constructs are restricted. Here are

some

phenomena

often-encountered in

a

domain-

constrained speech system.

word speech

Out-of-vocabulary words.

recognition systems [2]. Fuzzy logic

A user may speak words

has also been successfully applied to

that are not contained in

the description of words meanings as

the system lexicon.

related

external

Speech recognizer errors.

linguistic

This may match a word

descriptors have been used in control

into a wrong word, insert

systems, in which mappings can be

or delete a word, etc.

established

Flexible

sequences

generated

to

phenomena.

by

language Also

between

Fuzzy

fuzzy

linguistic

structures.

The

terms and physical quantities. Hot,

user may use expressions

cold, for example, can serve as labels

that

for fuzzy sets to which temperature

grammar does not cover.

the

system's

into

Disfluency.

membership degrees. Fuzzy logic rules

re-phrasing,

for control systems can accept fuzzy

words,

mis-pronounced

descriptors in both the premises and

words,

half-pronounced

the consequents to simulate human-

words, filled pauses, etc.

like inference.

These

The main goal of a speech recognition

system

system is efficient processing of speech

word semantic relations.

readings

can

be

mapped

could

False

start,

repeated

make

confused

the

about

recognition output.

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

4


Fuzzy Logic Based Term weighting

corresponding subset is rejected.

languages

are

For every subset that overcomes

history.

Panini

Term weighting (TW) is one of the

the threshold of certainty, the

grammar with 4000 rules for

major challenges in IE and IR. The

process is repeated. Now, the

Sanskrit. These are categorized

values

inputs to the FL engine are the

into different sets. One of them

related somehow to the importance

level

the

is Syadvada set. The Syadvada

of an index term in its corresponding

corresponding

terms and

set contains seven possibilities

set of knowledge in this case, Topic,

the process is repeated. The final

Section or Object. In FL based term

output

weighting scheme every index term

elements of the last level that is

has

to say, the objects whose degree

of

an

the

weights

associated

must

weight.

be

This

2

weights

for

index

corresponds

to

depending on the importance of term

definitive

in

shows the process in two level

level.

Greater

certainty

overcomes

threshold.

Figure

the

hierarchic

FL engine is used to determine the

Implementing FL engine obtained

degree of certainty or importance of

success to a great level.

structures.

Engine is the Degree of certainty. If degree of certainty lowers than a certain

threshold;

content

is

rejected.

history

in

have

World

long Natural

µSyadasti(x)^(1-µSyadasti(x)

^

µdifferenttimes(x; y))

same

define

indescribable.

for

Sanskrit

uncertainty information. It is not

hierarchic level 0, is divided into

possible to Computer processing

level 1 subsets. For each level 1

of

subset,

index

uncertain information. Grammars

certain

weights,

Sanskrit

language

with

and

is

µdifferenttimes(x;t)) ^ µdifferenttimes(x) 5.

Grammar

time

indescribable

languages. Panini was the first to

the

have

different times (Syadasti-nasti)

µSyadasti(x)^(1-µ)0 µSyadasti(x)

languages

knowledge,

must

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

Indian

fifth century. These rules contain

terms

Syad nasti = 1 - µSyadasti(x)

the

In this method, the whole set of constitutes

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

Fuzzy Modeling for Panini's Sanskrit Grammar

language with about 4000 rules in

which

µSyadasti(x) -> [0; 1]

4. May be it is and it is not at

a document for a given query. Index

input to FL Engine. Output of FL

they are given below.

4.1

importance means higher weight. A

term weight for every level act as

proposed

1. May be, it is. (Syadasti)

of

hierarchy

long

the

weight has a value between 0 and 1

every

having

May

be

it

is

and

yet

(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)

the

are defined to either programming

This fuzzy representation of the

possible inputs to an FL engine. If

languages or natural languages.

Sanskrit

sentences

the

Computer processing of natural

further

used

corresponding to a subset is lower

languages

reasoning.

than

translations is an application area

which

degree

a

of

predefined

threshold,

the

corresponding

CLEAR Dec 2012

are

value,

content

certainty

named of

the

and

language

For

shall

for

be

fuzzy instance,

consider two sentences

in the computer field. Indian Languages eld. I

5


May be, it is. (Syadasti)

Panini proposed grammar with 4000 rules for

May be it is, and it is not at different

Sanskrit. Fuzzy logic and fuzzy reasoning are

times (Syad asti-nasti)

discussed

to

deal

with

uncertainty

information in Panini's Sanskrit Grammar. The inference will be given as using R1

it is not at different times with the

References:

fuzziness (Syadasti) ^ (Syad asti-

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.

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

sequences

relations generated

to by

process speech

the

word

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

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

words, manipulation of perceptions is reduced to computing with words (CW). FL is applied for computation with words.

CLEAR Dec 2012

6


Indic Language Computing: A Review Author

‗exploring‘

Manu Madhavan

Government

M. Tech Computational Linguistics Govt. Engineering College, Sreekrishnapuram Palakkad

Palakkad In this

twenty

first

century,

where

Computation and Information technologies have

reached

as

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

visualized

by

effective.

The

not

translation other

systems

language

and

computing

solution is providing the technology

resources

in

different research institutes

their own

language.

People

developed

throughout the world have been

and

using computers and Internet in

enthusiasts, shows a hopeful

their

future.

own

languages.

Somehow,

volunteer

by

NLP

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

Challenges: Indian language computing has faced many challenges

more complex in India. The tools since

the

early

ages

of

present in the global market cannot language be

replicated

owing

to

computing

and

the even today. Let‘s go through

complexity

of

that

in

multiple

languages some

exist

the

country.

of

the

For challenges in Indic language

translation in Indian languages one-

applications.

computing. to-one mapping of each word as it is Dialects:

Through

the

innovations country

is

exploration

current

related

IT

promoting of

technological movement, the

electronic

our

maximum media

and

internet for reaching the people. But, in many

under-developed

Country,

people

only

areas know

of

their

mother tongue for communication,

the own

Apart

from

the

to form a sentence is not workable. typical

nature

of

Indian

The methodology to be followed languages,

cultures

also

here is to first process the source affect our language usage language, convert words according and

pronunciation.

For

to the target language, and then example, in northern parts process it all again with respect to of India, Hindi is spoken in the

target

language

for

the varied forms across different

conversion

to make sense. With states and cities. Thus we

these complexities, the current cannot have a generic tool,

CLEAR Dec 2012

7


especially for translation, and all tools

Indian

have to be developed for all of the

languages it has been transliterated

languages.

and retained as it is, experts of some

Corpus:

One

of

the

important

languages.

While

in

some

other languages went on to create a

resource for language computing is

whole

new

set

of

words

corpus. Some languages are spoken

corresponding to the IT terminology.

by large number of people, others by

Script:

a

scripts in digital format is difficult,

Shakti Standard Format Shakti Standard Format (SSF) is

a

highly

representation language

readable for

storing

analysis.

It

is

designed to be used as a small

group.

So,

getting

good

Representing

Unicode. The lack of standards in this

group to be computer savvy and

representation suppresses the use of

conversant in English as well as the

local languages in internet media.

extensible in which different

local language. This narrows down the

ISCII representation similar to ASCII

modules add their analysis.

number

for English is a standard developed

SSF

contacted for giving sample of the

for

analysis to be represented and

local lingo.

Government

Linguistic

Features:

can

be

Indian

Indian

Unicode

languages. of

standard

India

of

representation on which all

sample collection require the target

who

development

common format or common

even

people

the

Indian

corpus is difficult. The criteria for

of

with

the

Now accepted

characters

for

languages are morphologically richer

Indian languages. Transliteration for

than English. So, computing all the

Indian

valid inflections and derivations in

successful today. Indic languages are

language is challenging. A relief is

languishing

that the language is strictly structured

standardization

by well defined grammars, and the

technology.

languages

due

is

to and

considerably

lack

of

available

modules of a system operate. The

representation

also

operated

permits

upon

by

is

partial

different

modules. This leads to graceful degradation modules

in

fail

case to

some

properly

analyze a difficult sentence. (Developed

by

LTRC, IIIT-

Hyderabad)

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 phrases

jargons were

not

and

technical

grammatically

complete sentences, they were just computer commands. Also, words like document, folder, delimiters, add-ons are not enlisted in any dictionary of

CLEAR Dec 2012

‌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 ‌. 8


the process of adapting a software product to the linguistic, cultural and technical requirements of a process

is

target market. This

labor-intensive

and

often

requires a significant amount of time from the development teams. So in addition to translation, the localization process may Applications: One

also include adapting graphics to the target

prominent

use

is

the

digitization or creation of ebooks of the mounds of rich literature

in

languages.

different This

Indian

would

help

greater and better digitization of libraries

across

cultural

terrain----

documents

can

the

be

Indian Physical

converted

into e-documents and these can be further read out using textto-speech engines developed by private

companies

and

institutions.

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.

computing comes to play with the concept of cross-lingual search and the wordnet that are being developed by Pushpak

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

Bhattacharyya,

professor

of

computer science engineering at IIT-Bombay

and

head

of

Laboratory

for

Intelligent

Internet Access at the institute. localization

TDIL

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

Towards establishing a direct contact

and

providing

a

common platform to the larger community of people, including students,

linguists,

academicians

etc,

launched

the

and

Microsoft portal

"www.bhashaindia.com". portal

aims

at

This

building

a

community of developers and linguistic academia contribute

who will

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.

booking, helps the illiterate people. The

Another application of language

Software

markets, modifying content layout to fit the

Microsoft’s Bhashaindia

speech

systems

have

BhashaIndia, Indic portal

India‘s

computing has

leading

community

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

interesting

information

resources

like

of and

applications

related to Indic computing are all available on this site. Today BhashaIndia has become the destination

for

anybody

interested in Indian language computing.

interesting scope in Indian market. Ref : www.bhashaindia.com -Sreeejith C

defines Software localization as

CLEAR Dec 2012

9


Research Initiatives: Different

centers

Bangalore,

in

development

Mumbai,

language versions are some of

Language Interface Pack (CLIP)

and

the efforts from these volunteer

is a simple language translation

on

groups.

The

Indian

CLIP

Kolkata,

Thiruvananthapuram—work

Linux

area.

C-DAC—in

Pune,

project,

this

of

Noida,

SILPA

of

open

source

language computing technologies.

environment

Their

scope future development.

activities

include

provides

a

large

The

Microsoft

solution

that

Captions

uses

tooltip

captions to display results. Use CLIP as a language aid, to see

development of smaller utilities

translations in your own dialect,

like desktops and Internet access

Need for Tomorrow:

in

core

The major problem in this field is

machine

the lack of central co-ordinations.

update results in your own Indian

research

languages in

translation, access,

and

areas

of

OCR,

cross-lingual

search

standardization,

engines,

digital

library,

More

people

have

to

come

CLIP is designed to enable and

Government

take

support indigenous languages

teach

and native dialects and is the

has

to

necessary

steps

are also being seen as key players

language

computing

in the field -- including the IIT-

for engineering graduates. It is

Madras

been

very clear that the survival of

working and incubating innovative

language in the cyber world is

Indian-language

that

has

learning tool.

forward to work in this area.

and more. Other smaller groups

group

native tongue or use it as a

to

technology

result of the close collaboration between Microsoft and local communities. Users will be able to download multiple languages,

NCST

solutions,

the

essential to make the citizen a

Centre

for

global man.

(National

the

IIIT

target

translations

quickly and easily.

Software Technology) in Mumbai, and

switching

(International

To use,

simply move your

References:

mouse around the screen and

Technology) in Hyderabad, which

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

halt briefly over any text you

has

ex.php/component/content/article/

want translated. Users can also

521.html

add their own translations and

Institute

of

done

Information

impressive

machine-translation areas.

The

communities

work

and

works like

Malayalam

on

related of

NLP

Swathanthra

Computing(SMC),

International

Forum

for

Information

Technology

in

2.http://bhashaindia.com/Develop

copy and paste any results.

ers/Tutorial/Pages/IndianLanguage Computing.aspx

- Sreejith C

3.http://www.technologyreview.in/ computing/37921/

Tamil(INFIT), wikimedia etc are well appreciable and have key role

CLEAR Dec 2012

10


Natural Language Processing and Human Computer Interaction Natural

Author

Sreejith C

What makes this field Language as

NLP,

is

a

field

of

computer science. The field focuses on helping

computers

understand

interpret human languages.

and

Human

languages are also known as natural

Palakkad Over the past few years, our research

also really

abbreviated

M. Tech Computational Linguistics Govt. Engineering College, Sreekrishnapuram Palakkad

Processing,

NLP.

the

term

Computers are programmed to

try and interpret an input sentence in a

comprised of researchers from

natural language into a more formal

the

has

thus

been

both

group

languages,

Human-Computer

computerized

representation.

Many

NLP problems apply to both generation

Interaction (HCI) and the Natural Language

Processing

(NLP)

communities, and they have

and

understanding

languages. A computer must be able to understand the model of a natural language

thus been exploring how the two

natural

in

order

to

understand

it. Patterns in natural languages must

communities can benefit each

be programmed in order to produce a

other. This paper intends to

grammatically correct sentence in that particular natural language.

present several views on this

NLP

is

considered

to

have

great

topic, as well as some basic

potential

concepts and examples of how

corporate companies and governmental

the two disciplines meet in

to

provide

services

for

agencies. In present times, electronics are relied upon for many day-to-day

specific projects. This paper will focus on the relationships that can exist between HCI and NLP.

tasks

and

electronics

our

society

more

than

previous day.

it

relies

on

did

the

This high demand on

sophisticated electronics shows a need for technology such as NLP.

interesting

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 general.

in 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

information natural

CLEAR Dec 2012

is

using NLP.

more about languages

11


information about natural languages

draws from supporting knowledge on

using NLP.

both the machine and the human side.

Some neat technologies have been

On the machine side, techniques in

developed

computer

NLP.

to explore

the

field

of

For example, there are chat

systems,

graphics,

operating

programming

languages,

bots that can have conversations with

and development environments are

a

relevant.

human

or

another

bot.

These

On

the

human

side,

machines can learn more about how

communication theory, graphic and

humans

industrial

talk

to

each

other

and

design

disciplines,

simulate a human. Other applications

linguistics, social sciences, cognitive

include

psychology, and human factors such

tools

to

plagiarism...so

help

for

investigate

example,

if

I

as

computer

user

satisfaction

decided to simply copy and paste

relevant.

content from a small set of websites,

methods are also relevant. Due to the

programs can figure out that there is

multidisciplinary nature of HCI, people

a high relationship between the page I

with different backgrounds contribute

created and the websites that were

to its success. HCI is also sometimes

listed as a reference. This compilation

referred

of websites explores NLP by exploring

interaction (MMI) or computer–human

its history, its uses, and its side

interaction (CHI). A basic goal of HCI

effects, good and bad.

is to improve the interactions between users

Human–computer interaction Human–computer involves design

the of

Interaction

study,

the

planning,

interaction

(HCI) and

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–

Engineering

to

and

as

and

are

design

Relationship between HCI and Natural Language Processing To

answer

the

first

hot

question: Are HCI and NLP complementary fields? For that We

need

to

understanding

clarify

our

goals

and

of

methods of both disciplines. Indeed, it seems to us that the gap

can

only

explained

by

distinctions,

but

related

to

boundaries

partially

be

epistemic that

strong

it

is

discipline

separating

HCI

from AI.

man–machine

computers

by

making

computers more usable and receptive

Of course, HCI and NLP should meet in one obvious place: the natural language

to the user's needs. Researchers in HCI are interested in developing new design methodologies, experimenting with

new

hardware

prototyping

new software

exploring

new

interaction, and

devices,

language interfaces have several

systems,

paradigms developing

interface. Natural

for

models

advantages over direct manipulation

and theories of interaction.

computer interaction studies a human and a machine in conjunction, it

CLEAR Dec 2012

12


As a matter of fact, HCI and NLP

Yet, both HCI and NLG are concerned

probably

attempt to reach a common goal:

with

of

prominent. This is an obvious

simplifying user interaction with

communication,

see

instance where NLG and HCI

information systems. Despite this,

parallels

historically they have followed two

concerns. HCI design practitioners are

Speech interfaces are not the

antithetic design approaches. HCI

concerned

as

only point of contact between

is, by definition, user-centered;

information

and

HCI and NLG, though. Another

NLP has for long been based on a

differentiation, consistency with the

type of interface where the two

prevailing system-centered view.

ways users perform their tasks, and

disciplines

clear specification of the purpose of

which

HCI concentrates on interfaces,

each

is

interface. This is the case, for

artificial modules able to translate

analogous to ensuring in NLG that a

example, for web pages, or

digital

chunk of text is coherent and achieves

any form of hypertext. There,

signals

representations.

into

effectiveness and

between

with

we

can

their

such

various

issues

grouping

interface

element.

This

more

experts should collaborate.

meet

is

documents

one

in

act

as

focus

of

one or more specific communicative

interaction occurs within the

been

on

goals the user can recognize, and that

document/text.

users: interfaces adapt computers

a sequence of such chunks (or moves

related

to limits, capabilities and needs of

in a dialogue) is also coherent. Of

dialogue are important here,

humans. The focus of attention

course, HCI and NLP should meet in

so

has

one

natural

issues. An example of these

main

issues is the trade-off between

limits, capabilities and needs of

paradigm in HCI design today is direct

the number of hypertext links

humans. On the other hand, for

manipulation.

natural

the

many years NLP has focused on

language

several

arrive

systems, attempting to reproduce

advantages over direct manipulation:

information and the amount of

verbal

attention

has

always

interfaces

The

analog

the

become

always

been

adapt

on

users:

computers

communication

to

obvious

language

place:

the

interface.

The

However,

interfaces

have

to

are

While

language

other

user

and

interactional

must

at

issues

the

traverse

to

appropriate

at

the

they allow references to objects that

text to be presented at each

interface

by

are not directly visible and to events

point.

processing

that have occurred in the past or will

concerns

conversational inputs. In a perfect

occur in the future. In addition, with

new windows and whether the

NL system the traditional concept

the

small

old window disappears or not.

of

displays (e.g., mobile phones) and

A third example concerns the

mobile

way

human-computer architectures

user-interface

disappear:

the

tends

language

constitutes the interface.

CLEAR Dec 2012

to itself

increasing

devices,

number

vocal

of

interaction

between user and on-line services will

Another

a

the

example

positioning

hypertext

anchor

of

is

specified, and if and how

13


information about the target page

readily

accessible

should be provided. These issues

language form.

in

human

calculated responses which can

relate to the interface proper, and the interaction between the user

move the conversation on in an As NL will gain more importance in HCI, interaction will be less and less

and the computer.

a matter of pushing buttons and A Look into the Future: How NL Could Change HCI

a matter of specifying operations and assessing their effects through

work

the use of language. Computers will

natural

language

enable

communication

people

and

resorting

to

between

computers

to

is

without

no

longer

performing

be

medium

tasks

fully

where requires

of

users to define and execute all the

and

actions; computers will work at a

procedures. Automatic translation—

higher level, being able to split

enabling scientists, business people

actions in tasks and autonomously

and just plain folks to interact easily

executing them. The change can

with people around the world—is

deeply

another

goal.

So,

interaction: from doing to having it

continue

to

enable

complex

memorization commands

research humans

will to

apparently

meaningful

way

without requiring them to know what they are talking about.

dragging slides, and more and more

One goal for artificial intelligence in

them to use pre-prepared or pre-

done

affect

the

paradigm

consequently,

the

For example, if a human types, "I am feeling very worried lately," the

chatterbox

programmed

to

may

be

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?"

of

mental

communicate more naturally with

representation elicited by computers

their computers, with the ultimate

may drastically evolve.

goal being to determine a system of symbols, relations, and conceptual information that can be used by computer

logic

to

implement

Real Life Examples A

1. Chatter bots or Artificial Conversational Entities

similar

keywords

approach

would

be

using for

the

artificial language interpretation.NLP

A type of computer program that

program to answer any comment

has

for

simulates a real conversation via

including

translation, gaming, summarization,

auditory or textual methods; most

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

question

information

simply scan for keywords within

you?" Humans, especially those

creation.

input from human conversation and

Information management and data

create

querying would benefit hugely from

keywords

from

NLP.NLP can help with extracting

database.

They

and structuring text-based clinical

recognizing cue words or phrases

information, making clinical data

from the human user, which allows

continuing

retrieval,

implications

answering, and

robot

CLEAR Dec 2012

a

reply

(Name

unfamiliar

with

using

matching

sometimes

find

an

available

conversations

―converse‖

by

of

celebrity)

chatter the

bots,

resulting

engaging.

Critics

aren‘t impressed.

14


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

tell them jokes or simply talk with

Fetch or deliver items around

them.

the home or office Tidy up a room including

Robot-Nurse is too short to change picking up and throwing bedpans, but perhaps the later away trash versions will be able to free their Prepare meals using a human

counterparts

from

this normal kitchen

unpleasant chore. Use tools to assemble a

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

bookshelf

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

knowledge base for the robot.

technology (Paris et al., 1998).The 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.

projects emphasis was to try to address some of the limitations of current language technology that prevent its use in realistic settings.

Conclusion

In particular, our concern was with

In

the knowledge acquisition issue:

methodological

how

both

to

obtain

the

knowledge

conclusion,

HCI

and

from

point NLP

of

a view,

need

to

required for the generation of on-

upgrade their scientific apparatus

line help.

to cope with the design of social artifacts. It is well clear that the

Another way Robot-Nurse bonds with her patients is to keep those company who have no visitors to

CLEAR Dec 2012

4. Stair, the Stanford Robot

HCI and NLP communities should

University of Stanford is building a

work together on a wide variety

robot that can navigate home and

of problems. There are several

office environments, pick up and

areas where the cross-fertilization

interact with objects and tools,

can occur, and the combination of

and intelligently converse with and

the two types of expertise could

help people in these environments

be beneficial. Hence there is still

Over

Stair‘s

enough to research and Improve

creators envision a single robot

in this area, a promising future is

that can perform tasks such as:

waiting in this field.

the

long

term,

15


References: 1. http://www.cngl.ie/drupal/sites/default/files/papers 2/p4333-karamanis.pdf 2.

Antonella

De

Angeli

and

Daniela

Petrelli,

Inviting Article for CLEAR March 2013

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,

We are inviting thought-provoking articles, interesting dialogues and healthy debates on

Trieste, Italy 3. Cile Paris and Nadine Ozkan ― Motivating the cross-fertilization

between

HCI

and

Natural

Language Processing “, CSIRO/MIS Locked bag 17,

multifaceted aspects of Computational Linguistics, for the forthcoming issue of CLEAR (Computational Linguistics in Engineering And

North Ryde NSW 1670, Australia. 4. https://sites.google.com/site/naturallanguageproce

Research) magazine, publishing on March 2013. The topics of the articles would preferably be

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.

related to the areas of Natural Language Processing, Computational Linguistics and

htm 7. De Angeli* and Daniela Petrelli ―Bridging the gap between NLP and HCI: A new synergy in the name of the user‖,

Information Retrieval. The articles may be sent to the Editor on or before 15th February, 2013 through the email simplequest.in@gmail.com.

8. Do HCI and NLP Interact? CHI 2009 ~ Spotlight on Works in Progress ~ Session 2 April

4-9, 2009 ~

-Editor

Boston, MA, USA

CLEAR Dec 2012

16


Google Driverless Car 1,

Author

and

Department

Robert Jesuraj K M. Tech Computational Linguistics Govt. Engineering College, Sreekrishnapuram Palakkad

of

the

Nevada

Motor

Vehicles

is

now

Prius

existing law, some of which

Google's

dates back to the era of

issued

to

a

Toyota

with

technology

plans to commercially develop the

project is currently being led by Google

system,

engineer Sebastian Thrun, director of

develop a business which would

the

market the system and the data

Intelligence

technology

was

While Google had no immediate

Artificial

because

is in danger of outstripping

by Google that involves developing

Stanford

reality

driven car in May 2012. The license

experimental driverless technology.

cars. The

"the

a

advancing so quickly that it

Google driverless car is a project

for driverless

become

issued the first license for a self-

modified

Palakkad The

2012,

the

it

company

to

hopes

to

Laboratory and co-inventor of Google

behind

automobile

Street View. Thrun's team at Stanford

manufacturers. An attorney for the

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

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. system

The

team

consisted

of

developing 15

the

engineers

working for Google, including Chris Urmson,

Mike

Montemerlo,

and

Anthony Levandowski who had worked

California

Motor

amendment to an electric

on

Vehicles raised concerns that "The

vehicle bill that provides for

technology is ahead of the law in

the licensing and testing of

many areas," citing state laws that

autonomous vehicles. The

"all presume to have a human

second bill will provide an

being operating the vehicle". to the

exemption from the ban on

New York Times, policy makers and

distracted driving to permit

have argued that new laws be

occupants

required if driverless vehicles are to

messages

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

Department

of

to

send

while

text sitting

behind the wheel.

CLEAR Dec 2012

17


The two bills came to a vote before the

a human driver to take control

technician in the passenger

Nevada state legislature‘s session ended

by stepping on the brake or

seat to monitor the navigation

in June 2011. It has been speculated

turning the wheel.

system, seven test cars have

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.

concerning the operation of driverless cars in Nevada, which went into effect March

modified

1,

2012. A

with

Google's

Toyota

Prius

experimental

driverless technology was licensed by the

Nevada

Department

of

have

driverless about

test

cars

$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

Motor

Vehicles (DMV) in May 2012. This was the first license issue in the United

on

vehicles

that

themselves, intelligence

using software

can

drive

artificialthat

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

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

working in secret in plain view

Nevada passed a law in June 2011

on

driven Google's

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.

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

CLEAR Dec 2012

18


Robot

drivers

react

faster

than

variety of sensors and following a route

car,

humans, have 360-degree perception

programmed

where

and do not get distracted, sleepy or

system nimbly accelerated in the entrance

intoxicated, the engineers argue. They

lane and merged into fast-moving traffic

speak in terms of lives saved and

on Highway 101, the freeway through

injuries avoided — more than 37,000

Silicon Valley.

into

the

GPS

navigation

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 reliable

cars must be

far more

than, say, today‘s personal

computers, which crash on occasion and are frequently infected.

artificial the

intelligence

automobile

is

to

revolutionize

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

it

is

more

Christopher Urmson, Carnegie

University

Mellon robotics

It drove at the speed limit, which it knew

scientist, was behind

because the limit for every road is included

the

in its database, and left the freeway

using

several exits later. The device atop the car

control of the car he

produced

has

a

detailed

map

of

the

environment.

wheel it.

to

but To

do

not gain

one

of

three things: hit a red button near his

The car then drove in city traffic through Mountain View, stopping for lights and stop

signs,

announcements

as like

well

as

making

―approaching

a

crosswalk‖ (to warn the human at the wheel) or ―turn ahead‖ in a pleasant

The Google research program using

aggressive,

likely to go first.

a

people died in car accidents in the United States in 2008. The engineers

to

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

right hand, touch the brake

or

steering

turn

the

wheel.

He

did so twice, once when a bicyclist ran a red light and again when a car in front stopped to

and began

back

parking

into

space.

a But

the car seemed likely to have prevented an accident itself.

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

CLEAR Dec 2012

"...using the infinity symbol was the best way to represent the 'car of the future'."

19


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

20


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

Author

Octave interprets "i" to identify the irrational part,

Razee Marikar

it understands constants like pi, and it interprets

Subex Azure Limited, Bangalore

"^" as the power function.

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

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

for

page at 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

You can also store results to a variable. Here are some examples:

Octave-3.2.4.exe:12> a = 10 Octave-3.2.4.exe:13> ans = 100 Octave-3.2.4.exe:14> (2.2 + 3.1i) * (10 + b = 18.800 + 40.400i Octave-3.2.4.exe:15> c = 188 + 404i Octave-3.2.4.exe:17> Octave-3.2.4.exe:18> c = 230 + 404i

a=10 a*a b=(3 + 5i) + 2i) c = a*b c = a*b+42; c

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.

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.

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

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(3Ď€+5i)Âł. 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:

CLEAR Dec 2012

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 5 7

2 7 8

3 2 0

21


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

References and further reading: 1. Official documentation here:

1.06667 -0.93333 0.60000

-1.60000 1.40000 -0.40000

1.13333 -0.86667 0.20000

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

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

Octave-3.2.4.exe:23> A + B ans = 6 6 6

9 7 11

on the Tutorial Guide to Matlab written by Dr. Paul Smith:

8 3 5

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

Octave-3.2.4.exe:24> A * B ans = 3. Machine Learning classes available online by

4 30 43

16 41 49

22 42 43

Stanford University (Prof. Andrew Ng)

Octave-3.2.4.exe:25> 2*A ans = 2 10 14

4 14 16

6 4 0

Octave-3.2.4.exe:26> B/A ans = 1.80000 1.66667 -0.86667

-0.20000 -2.00000 3.80000

0.60000 1.33333 -2.73333

Using the above examples, it should be evident how this can be used to solve numeric equations.

CLEAR Dec 2012

22


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.

L

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

A

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

S T

engineering graduate. Moreover, people should have an enthusiasm on their language, not to divide themselves, but to join the global technology.

W

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

O

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 ‗

R

‘, you put for the last issue of CLEAR. This motivated

Simple Groups to bring the second issue.

D

Expecting your future supports! Wish you all the best....

Manu Madhavan

CLEAR Dec 2012

23


CLEAR Dec 2012

Profile for Simple Groups

CLEAR Dec 2012  

Computational Lingusitcs in Engineering And Rsearch (CLEAR) Vol.1 Issue 2

CLEAR Dec 2012  

Computational Lingusitcs in Engineering And Rsearch (CLEAR) Vol.1 Issue 2

Profile for simplegec
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