Fifth World Sanskrit Conference Sanskrit Studies in India 1979 - 1981 - Rashtriya Sanskrit Sansthan
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![Page 1: Sanskrit and Natural Language Processing Dr.Srinivasa Varakhedi Center for Advanced Studies and Research in Shabdabodha and NLP RASHTRIYA SANSKRIT VIDYAPEETHA.](https://reader035.fdocuments.us/reader035/viewer/2022081419/56649e015503460f94aea5a7/html5/thumbnails/1.jpg)
Sanskrit and Natural Language Processing
Dr.Srinivasa VarakhediCenter for Advanced Studies and Research
in Shabdabodha and NLP
RASHTRIYA SANSKRIT VIDYAPEETHA
DEEMED UNIVERSITYTirupati(A.P)
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Dream of a bee…..
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{ÉRÂóEòVɸÉÒ&**<ilÉÆ Ê´ÉÊSÉxiɪÉÊiÉ EòÉä¶ÉMÉiÉä Êuù®äú¡äò*
½þÉ ½þxiÉ ½þxiÉ xÉʱÉxÉÓ MÉVÉ =VVɽþÉ®ú**
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Present situation of Sanskrit Sanskrit colleges are like 'zoo'! No Govt. support unless we are
productive Humanities and Languages are being
neglected How far this support will continue ? Great tradition of learning is being
lost No scope for novel research
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Innovation is the key Sanskrit Shastras are competent
enough to enter the science world Move out of Humanities and get
merged with science Analogy : Maths, psychology, Logic. We must find practical approach for
these 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 Technology fortunately these shastras are found relevent
in today’s technology
Computing ideas in Panini Text processing principles in Meemamsa Formal languages in Nyaya
we lack the technology and application area
Story of Babbage!!!
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Massage of Acharya Shankara Bhagavatpada
“avidyayaa mrtyum tiirtvaa..vidyayaa amrtamashnute..”
- Ishavasya Uapanishad
Sri Shankara Bhagavatpada comments on this …..
avidyaa = karma ; vidyaa = knowledge
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Opportunity Emerging Info technology has provided
a great oportunity to survive MÉÞàþÒªÉÉiÉ ÊiÉxiÉÞhÉÒ¶ÉÉJÉÉÆ
ʶÉOÉÖ¶ÉÉJÉÉOɽäþhÉ ÊEò¨É ? Solve a major contemporary problem
like MT basing on the shastras Get new openings for Sanskritists Open a new avenue for research
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Know How… Ultimate aim :finding appropriate place
for sanskrit Shastras
Method: solutions to contemporory problems adopting modern technology
Resource needed : Adequate manpower, who act as a bridge between modern scientists and technologists one side and sanskrit scholars on the other side.
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Change the scenario
Technology
Western Theories INDIAN THEORIES
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Opportunities missed Industrial revolution
We missed this with some hasty decisions
IT revolution Indians are serving in the level of
coding ; not in designing level ! Knowledge Revolution
we should take this advantage
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Need of the hour we need
to understand how technology works to understand the contempomporary
problems Then
we will be able to give solutions in the light of sashtras and show the relevence of Indian theories
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History and Progress Conference held at Bangalore in Dec 1987
on “Knowledge Representation and Sanskritam” generated tremendous interest
Nothing much has been archived, except some efforts and projects here and there in small scale that too in technical institutions
Time running out ! What progress has been made since then?
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Complexity of the problem Different Goal : Two disciplines – Technology
and Shastras - are developed in different context
Paradigm difference : Modern Scholars are accustomed to visual teaching method, Traditional Pandits on the other hand prefer oral tradition
Language Barrier : Both of them do not understand each other’s language !
The tuning in of the dialogue will take time
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Who would bell the cat ? It needs a long interaction between
technologists and Traditional Sanskrit Scholars
Technical institutions are always ready for such activities
There is NO much interest is seen in Sanskrit Institutions
It 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
It’s a big challenge, one OR two persons are not enough
We need hundreds of dedicated persons to achieve a small goal
A person can climb a small hill ; Team can climb the 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. (+{ÉÉä½þ) -
¶ÉÉJÉÉSÉxpù¨ÉºÉɯûxvÉiÉÒxªÉɪÉ&
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Possible areas Machine Translation Speech Processing Summary Extraction from huge texts Indo Wordnet as a base for IL-wordnets Developing Tools for IL Researchers Knowledge Representation schemes
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Machine Translation English To Indian Languages
Word sense disambiguation Karaka & Syntax Relation Word-grouping Idiomatic Expression Shabdasutra
MT among Indian Languages Bi-language Electronic Dictionaries Karaka & Vibhakti Relation
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Major MT systems
India Angla-Bharati, IIT Kanpur Shakti, IIIT Hyderabad Mantra, CDAC Pune SaHiT (Sanskrit Hindi Translator),
CSS, JNU Anusaaraka (RSV, HCU, IIIT)
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Major MT systems Outside India
UNITRAN BabelFish 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 at http://ourworld.compuserve.com/homepages/WJHutchins/Compendium-11.pdf
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Summary Extraction Meemamsa Principles applied to
extract the summary of a text
Upakramaadi Tatparya Lingas are used to extract the summary of a text in Indian Institute of Science, Bangalore, in our consultancy.
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Wordnet / Concept-net based on NN ontology Wordnet is an electronic lexical
reference resource system designed on the basis of semantic relations of words Synonymy {Graha, nivaasa,….} Hypernymy {Amra, vriksha, vanaspati…} Antonnymy {Shreemaan, akinchana} Mecronymy {nAsika, mukha, shariira..} Gradation {Shushka,…tara,….tama}
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Knowledge Engineering Representation
For Data representation, several databse management systems are available.
For representing and retrieving useful information, there are various worked out methodologies
Finally Knowledge Representation needs special treatment where Indian Knowledge systems can be applied
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Knowledge and its importance in AI AI researchers are interested in building
Intelligent systems Web technologies looking forward to
Semantic webs instead of syntactic web Knowledge is more valuable than data
and Information Data – simple DoB. Info – Age calculated.
Knowledge – the judgment about suitability for job at hand etc. This requires a lot of inputs from various K- sources.
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Computational Linguistics and Panini’s Grammar The structure of Paninian Grammar is
nothing but a computer program – Babbage !
It has captured the base of universal principles of all languages
CL requires formal rules for analysis and generation of language
Slowly Chomsky and others are turning towards Panini…
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The System of Panini Phonetic component
Phonemes pratyahara
Rule base Vidhi (operations) Samjna paribhasha (metarules) adhikara (headings) atidea (extension) niyama (restriction)
Lexicon Dhatupaatha Ganapaatha
Lists Affixes Rule specific items
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Paninian Model for Sentence Analysis
Action – Central theme Karakas – Syntactico-semantic roles Visheshana-Visheshyabhava Concept of anabhihite…in switching
to different voice Vivakshaa – Intention of speaker Form and meaning
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Navya Nyaya -> AI ?
Classify Nyaya into five parts …..1. Ontology2. Epistemology3. Technical Language4. 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 Cognitive sciences.
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Epistemology
Deals with … Cognitive process Cognitive structure
It helps to solve the problems of cognitive sciences and K-repr.
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Technical Language NNL is a Restricted Language that
has both the features – power of mechanism of Artificial Languages and power of of expression of Natural Languages.
The basic ideas behind this language will be helpful in Knowledge Represenation.
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Semantics Way of analysis of semantics shown by
Navya Naiyayikas has been crucially found helpful in NLP and Machine Translation
Eg. Classification of words – rUdha, yogaSyntactical analysisPower of definitions
KR & NN
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Semantics in MT Lexicography
Word/concepts nets based NN ontology
Classification of pada’s (words) Rudha – word has convention I.e names… Yougik – word has etymological
meaning…cook, driver, Yoga-rudha – which has etymology as
well as convention…CD-driver
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WSD – using different techniques Definitions of Karaka relation
without any overlap Kartrtvam = kriyAnukUlakritimattvam Karmattvam = 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 Relations Classification of Karma
Karma – Reachable, understandable so on.
Analysis of root semantics Leave – He left the place / left from
the place Analysis of expectancy (AkAnkshA)
Rats killed cats
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To infinity relation I stand up to speak I want o speak He goes to London to study law He wants to study law in London To walk in mornings is good for
health
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Special thanks to The authorities of
Sri Chandrashekharendra Sarasvati Vishvamahavidyalaya
Kanchipuram
Namaste!