SAnskrit and NLP@Kanchi

7/28/2019 SAnskrit and NLP@Kanchi

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SanskritandNatural Language Processing

Dr.Srinivasa VarakhediCenter for Advanced Studies and Research in

Shabdabodha and NLP

RASHTRIYA SANSKRIT VIDYAPEETHA DEEMED UNIVERSITY

Tirupati(A.P)


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Dream of a bee..

j& Mi i |i*x =ni i {REV&**


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Present situation of SanskritSanskrit colleges are like 'zoo'!No Govt. support unless we areproductiveHumanities and Languages are beingneglected

How far this support will continue ?Great tradition of learning is being lostNo scope for novel research


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Innovation is the keySanskrit Shastras are competentenough to enter the science worldMove out of Humanities and getmerged with science

Analogy : Maths, psychology, Logic.We must find practical approach forthese Sanskrit Sciences.


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we have lost 80%Meemamsa - No practical approach !

Nyaya - No use in modern dialectics ?

Vyakarana No application ??

What to do ?


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Relevance of Sanskrit Shastras in

Modern Technologyfortunately these shastras are found releventin todays technology

Computing ideas in PaniniText processing principles in MeemamsaFormal languages in Nyaya

we lack the technology and applicationarea

Story of Babbage!!!


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Massage of Acharya Shankara

Bhagavatpada avidyayaa mrtyum tiirtvaa..

vidyayaa amrtamashnute.. - Ishavasya

UapanishadSri Shankara Bhagavatpada comments on

this .. avidyaa = karma ; vidyaa =knowledge


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OpportunityEmerging Info technology has provided agreat oportunity to survive

Mi ixihJ OJOE ?

Solve a major contemporary problem like MTbasing on the shastrasGet new openings for SanskritistsOpen a new avenue for research


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Know How Ultimate aim :finding appropriate place forsanskrit Shastras

Method: solutions to contemporory problemsadopting modern technology

Resource needed : Adequate manpower, whoact as a bridge between modern scientistsand technologists one side and sanskritscholars on the other side.


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Change the scenario

Technology

Western TheoriesINDIAN THEORIES


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Opportunities missedIndustrial revolution

We missed this with some hasty decisions

IT revolutionIndians are serving in the level of coding ;not in designing level !

Knowledge Revolutionwe should take this advantage


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Need of the hourwe need

to understand how technology worksto understand the contempomporaryproblems

Thenwe will be able to give solutions in the lightof sashtras and show the relevence of Indian theories


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Complexity of the problemDifferent Goal : Two disciplines Technology andShastras - are developed in different contextParadigm difference : Modern Scholars are

accustomed to visual teaching method, TraditionalPandits on the other hand prefer oral traditionLanguage Barrier : Both of them do not understandeach others language !

The tuning in of the dialogue will take time


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Who would bell the cat ?It needs a long interaction betweentechnologists and Traditional Sanskrit

ScholarsTechnical institutions are always ready forsuch activitiesThere is NO much interest is seen in SanskritInstitutionsIt is we Sanskritists should to bell the cat


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Long process like extraction of

ghee from milk Nothing miracle happens in the initial stage

Its a big challenge, one OR two persons arenot enough

We need hundreds of dedicated persons toachieve a small goal

A person can climb a small hill ; Team can climbthe Everest


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Identifying the problem Analogy:- Braman in Upanishads

what is Brahman?we can NOT show it as it is impercievable.we can NOT describe it as it is beyond words.

Hence ,we can direct you towards that by way of negating what we know.

(+{) - JSxpxvix&


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Possible areasMachine TranslationSpeech ProcessingSummary Extraction from huge textsIndo Wordnet as a base for IL-wordnets

Developing Tools for IL ResearchersKnowledge Representation schemes


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Machine TranslationEnglish To Indian Languages

Word sense disambiguation

Karaka & Syntax RelationWord-groupingIdiomatic ExpressionShabdasutra

MT among Indian LanguagesBi-language Electronic DictionariesKaraka & Vibhakti Relation


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Major MT systemsIndia

Angla-Bharati, IIT KanpurShakti, IIIT HyderabadMantra, CDAC Pune

SaHiT (Sanskrit Hindi Translator), CSS,JNU

Anusaaraka (RSV, HCU, IIIT)


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Major MT systemsOutside India

UNITRANBabelFish AltaVista (Systran)

ATR (bimodal, Japan)JANUS (bimodal, US-Germany)SLT (SRI, Cambridge)

VERBMOBIL (Germany)DIPLOMAT (Carnegie-Mellon)

Get a 125 page directory of available MT systems athttp://ourworld.compuserve.com/homepages/WJHutchins/Compendium-11.pdf
http://ourworld.compuserve.com/homepages/WJHutchins/Compendium-11.pdfhttp://ourworld.compuserve.com/homepages/WJHutchins/Compendium-11.pdfhttp://ourworld.compuserve.com/homepages/WJHutchins/Compendium-11.pdfhttp://ourworld.compuserve.com/homepages/WJHutchins/Compendium-11.pdf


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Summary ExtractionMeemamsa Principles applied to extractthe summary of a text

Upakramaadi Tatparya Lingas are usedto extract the summary of a text inIndian Institute of Science, Bangalore,in our consultancy.


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Wordnet / Concept-net

based on NN ontologyWordnet is an electronic lexicalreference resource system designed on

the basis of semantic relations of wordsSynonymy {Graha, nivaasa,.} Hypernymy {Amra, vriksha, vanaspati}

Antonnymy {Shreemaan, akinchana}Mecronymy {nAsika, mukha, shariira..}Gradation {Shushka,tara,.tama}


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Knowledge EngineeringRepresentation

For Data representation, several databse

management systems are available.For representing and retrieving usefulinformation, there are various worked outmethodologies

Finally Knowledge Representation needsspecial treatment where Indian Knowledgesystems can be applied


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Knowledge and its importance in

AI AI researchers are interested in buildingIntelligent systemsWeb technologies looking forward toSemantic webs instead of syntactic webKnowledge is more valuable than data andInformationData simple DoB. Info Age calculated.Knowledge the judgment about suitabilityfor job at hand etc. This requires a lot of inputs from various K- sources.


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Computational Linguistics and

Paninis Grammar The structure of Paninian Grammar is nothingbut a computer program Babbage !

It has captured the base of universalprinciples of all languagesCL requires formal rules for analysis andgeneration of languageSlowly Chomsky and others are turningtowards Panini


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The System of PaniniPhonetic componentPhonemespratyahara

Rule base

Vidhi (operations)Samjnaparibhasha (metarules)adhikara (headings)atide a (extension)niyama (restriction)

LexiconDhatupaathaGanapaatha

Lists AffixesRule specific items


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Paninian Model for Sentence

Analysis Action Central themeKarakas Syntactico-semantic roles

Visheshana-VisheshyabhavaConcept of anabhihitein switching todifferent voice

Vivakshaa Intention of speakerForm and meaning


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Navya Nyaya -> AI ?Classify Nyaya into five parts ..

1. Ontology2. Epistemology3. Technical Language

4. Semantics5. Art of debate and fallacies


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OntologyIncludes

Categories - Substance, Quality etc.,Relations SamavAya, SvarUpa Universals Types or classes

Ontology helps to various areas like NLP,K-Repr, K-Engg, especially in Cognitivesciences.


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EpistemologyDeals with

Cognitive processCognitive structure

It helps to solve the problems of cognitivesciences and K-repr.


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Technical LanguageNNL is a Restricted Language that hasboth the features power of

mechanism of Artificial Languages andpower of of expression of NaturalLanguages.

The basic ideas behind this language willbe helpful in Knowledge Represenation.


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SemanticsWay of analysis of semantics shown by NavyaNaiyayikas has been crucially found helpful in

NLP and Machine Translation

Eg. Classification of words rUdha, yoga

Syntactical analysisPower of definitionsKR & NN


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Semantics in MTLexicography

Word/concepts nets based NN ontologyClassification of padas (words)

Rudha word has convention I.e names Yougik word has etymologicalmeaningcook, driver,

Yoga-rudha which has etymology as well asconventionCD -driver


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WSD using different

techniquesDefinitions of Karaka relation withoutany overlap

Kartrtvam = kriyAnukUlakritimattvamKarmattvam = para-samaveta-kriyA-janya-phala-Ashrayatvam

Going Rama and Forest

Who is going where ?Result contact is possible in Rama too..To avoid such overlap, this def. Is useful


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Refinement of karaka

RelationsClassification of Karma

Karma Reachable, understandable so on.

Analysis of root semanticsLeave He left the place / left from theplace

Analysis of expectancy (AkAnkshA)Rats killed cats


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To infinity relationI stand up to speak I want o speak He goes to London to study lawHe wants to study law in London

To walk in mornings is good for health


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Special thanks toThe authorities of

Sri Chandrashekharendra Sarasvati VishvamahavidyalayaKanchipuram

Namaste!

SAnskrit and NLP@Kanchi

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