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  1. Machine Translation Speaker Prof.. Rajeev sangal International Institute of Information Technology, Hyderabad Indo-German Workshop on Language technologies AU-KBC Research Centre, Chennai

  2. CALTS, UNIV. OF HYDERABAD. SAP, LANGUAGE TECHNOLOGYDr. Uma Maheswara Rao University of CALTS has been in NLP for over a decade. It has participated in the following major projects: 1.NLP-TTP, DOE Govt. of India. 2.IPDA, DOE Govt. of India. 3.TRCT, TDIL, MCIT 4.English-Telugu, T2TMT UPE, UGC, UOH.

  3. 1.Morphological Analyzer cum Spell Checker for Telugu • A robust Morphological analyzer cum Spell Checker for Telugu. • With 97% recognition rate. • Tested on 5 million word corpora. • For the users of Windows O.S & Linux.

  4. 2.A Multilingual Encyclopedic Electronic thesaurus for translators, MEET, a Web based linguistic application. • MEET enables quick access to various synonyms. • Provides equivalents in other Indian languages and English. • Also provides grammatical and Semantic information. • A useful application for translators. • Provides access to information in Indian languages on the web. • Currently includes only Marathi, Hindi, Bangla, Konkani and English. • The 2nd phase proposes to include Telugu, Kannada and Oriya. • Word net for individual languages may be linked to the system.

  5. 3.   Telugu Hyper Grammar. • The Telugu Hyper Grammar, designed as a dynamically accessed and non-linearly organized grammar of Telugu grammar. • A user can access information at a particular module from any other module. • Provides access to a Morphological Analyzer, Generator and a Chunker. • Can access various bilingual and bi-directional digital lexica of Telugu and other Indian Languages like Hindi, Kannada, Tamil, Marathi, Oriya, Malayalam and English.

  6. 4.English-Telugu Parallel Corpora. • Parallel Corpora are a set of thematically corresponding digital texts of some selected works. • Recent trends in Machine Translation are revolutionized by the use of Parallel Corpora. • Parallel Corpora give way to discover similarities and differences between a pair of languages. • A program for aligning parallel texts in English and Telugu is developed and in the process of testing. • Selected parallel texts in Telugu, Kannada, Tamil, Marathi and Malayalam are digitized.

  7. 5.English-Telugu T2T Machine Translation System • English-Telugu Machine Translation System is being built at CALTS in collaboration with, IIIT, Hyderabad; Telugu University, Hyderabad; Osmania University, Hyderabad. • Uses an English-Telugu MAT lexicon of 42K. • A wordform synthesizer for Telugu is developed and incorporated. • It incorporates an evolutionary semantic lexicon • It handles English sentences of a variety of complexity

  8. 6.MAT Lexica. • Bilingual and Multidirectional. • Machine Readable Dictionaries for Telugu-Hindi, Telugu-Kannada, Telugu-Tamil, Telugu-Marathi, Telugu-Oriya, Telugu-Bangla, Telugu-Malayalam, of 10K are being developed in collaboration with the Telugu Academy. • The entries were based on the frequency of their occurrence in the corpus of Telugu. • The Dictionaries of Telugu-Hindi, Telugu-Kannada, Telugu-Tamil are already completed. • Major part of these dictionaries are developed through realigning the lexical resources existing at CALTS.

  9. 7.Collocations in Indian Languages. • Collocations or specialized word sequences play a crucial role in a language. It is extremely difficult to identify and translate effectively. They present one of the most challenging tasks in Natural Language Processing. • In the first phase, Telugu data was collected and analyzed. • A long list of collocations are collected and checked whether the existing criteria are valid. • These collocations are compared against other specialized word sequences in the language to understand their functional and distributional properties.

  10. 8.Machine Readable Dictionary of Idioms (Telugu-English). • Idioms are extremely important but the most ubiquitous, and less understood categories of language. • Machine-readable Idioms in English and their equivalents in Telugu and the mechanics of their recognition and transfer rules are being developed. • The Machine Readable text will be implemented in XML so that access and retrieval becomes easier and faster.

  11. 9.Electronic Adult Literacy Primer for Telugu • This is developed as part of CALTS participation in Arohan (a literacy campaign adopted by the university). • Aimed at teaching the script or the written form of the language rather than the language itself. • Based on frequency of characters in the written texts. • Learning the most frequent but few characters would ensure greater coverage in learning recognition of characters. • Special features include characters with animation and speech. • A special attention on the presentation of allographs.

  12. 10.A generic system for morphological generation for Indian languages • Morphological generators for various Indian languages particularly for Telugu, Kannada, Tamil, Malayalam, Bangla and Oriya are in different stages of development. • A generic framework for wordform synthesis for Indian languages. • Includes testing module to find the efficiency and coverage of the system.

  13. 11.Telugu-Tamil Machine translation system • Using the available resources at CALTS a Telugu-Tamil MT is being developed. • Uses the Telugu Morphological analyzer. • Uses the Tamil generator developed at CALTS. • Uses Telugu-Tamil dictionary developed as part of MAT Lexica. • Uses verb sense disambiguator based on verbs argument structure.

  14. 12.Word Sense Disambiguation using Argument Structure: • A system, based on the argument structure of Telugu verbs. • Uses feature based semantic lexicon. • Efficiently disambiguates polysemy of verbs in the context. • Is incorporated in Telugu-Tamil MT system.

  15. 13.A case sensitive roman translation for Indian languages as overall pattern • A roman transliteration Scheme for unwritten languages of India is developed. • A common transliteration scheme for the scripts of Brahmi derivates and non Brahmi derivates is developed. • Supra segmentals mapped on to roman characters • No nonunique character mapping • Allows complete conversion between various languages

  16. Language Engineering ResearchatResource Centre for Indian Language Technology SolutionsUniversity of HyderabadDr. K. Narayana Murthy University of

  17. So far • UCSG System of Syntax, Parsers • English-Kannada Machine Aided Translation • OCR for Telugu and other Indian Languages • Telugu Corpus (10 Million Words) • Experimental Text-to-Speech System for Telugu • A Variety of tools

  18. Architecture of a Hybrid Machine Translation System SL Sentence TL Sentence Tagger(HMM) Post Editing Identify/Rate Word Groups (FSM, Markov Models, MI) Syntactic Generator Identify Clause Structure Assign Functional Roles to Word Groups Structural Description (TL Inst.) (The Phrases & their Roles of each clause) Rate/Rank Role Assignments Best First Search for Best Parse Structural Description (SLInst.) (The Phrase & their Roles for each Clause) TL Sentence Planner Clause/Phrase/Word level Transfer (WSD Statistics)

  19. Research Activities Department of Computer Science & Engineering College of Engineering, Guindy Chennai – 600025 Participant : Dr.T.V.Geetha Other members: Dr. Ranjani Parthasarathi Ms.D. Manjula Mr. S. Swamynathan

  20. Natural Language Processing Translation Support Systems Work done in the area • Morphological Analyzer & Generator for Tamil • Tamil Parser • Tackles both simple and complex sentences.  Can handle sentences with a noun clause and multiple adjective and adverb clauses.   • Universal Networking Language (UNL) for Tamil • At present all the UNL relations have been handled and simple sentences can be processed.  Both Tamil to UNL and UNL to Tamil have been handled • Heuristic Rule based Automatic Tagger • Tagger works without a dictionary and it is based morphological heuristic rules and certain amount of lookahead

  21. Natural Language Processing, Translation Support Systems - Possible Areas of cooperation • Tamil Sentence generator • Incorporation of grammatical structures to facilitate sentence formation • Design of a format to be given as input to sentence generator • Generation of complex sentences Tamil Parser and Semantic Analyzer • Tackling of complex grammatical structures • Case based semantic analysis of simple sentences • Tackling of ambiguous and incorrect sentences by the parser

  22. Natural Language Processing Group Computer Sc. & Engg. Department JADAVPUR UNIVERSITY KOLKATA – 700 032, INDIA. Professor Sivaji Bandyopadhyay

  23. Research Areas • Natural Access to Internet & Other Resources • Headline Generation • Headline Translation • Document Translation • Multilingual Multidocument Summarization • Cross-lingual Information Management • Multilingual and Cross-lingual IR • Open Domain Question Answering

  24. Natural Access to Internet & Other Resources • Headline Generation • A machine translation problem • the input document identified by a set of features and output headline represents some of them • Example Base • Set of features in the input document and the headline template(s) • Implemented for generating headlines from cricket news in English

  25. Natural Access to Internet & Other Resources • Headline Translation • A Hybrid MT system for translating English news headlines to Bengali • Syntactic and Semantic classification of news headlines done • Anaphora and Coreference classes identified in news headlines • Translation Strategy The input headline first searched in Translation Memory, else tagged and searched in Tagged Example Base, else analyzed and matched in Phrasal Example Base, else heuristics applied

  26. Natural Access to Internet & Other Resources • Document Translation • Prototype developed for A Hybrid MT system from English to Bengali • Translation Strategy • Identify the constituent phrases of a sentence using a Shallow Parser • translate them individually using an Example Base • arrange the translated phrases using heuristics to form the target language output • Verb phrases translated using Morphological Paradigm Suffix Tables

  27. Natural Access to Internet & Other Resources • Multilingual Multidocument Summarization • Multidocument summarization in each language • Summarize one of the documents using extraction methods • Revise the summary using other documents • Summary in the target language is the reference summary • Translate all summaries to the target language • Revise the reference summary

  28. Efforts in Language & Speech Technology Natural Language Processing Lab Centre for Development of Advanced Computing (Ministry of Communications & Information Technology) ‘Anusandhan Bhawan’, C 56/1 Sector 62, Noida – 201 307, India

  29. Translation Support System Technology : Angla Bharati (Rule base) developed by IIT Kanpur. System developed jointly by IIT,Kanpur and CDAC Noida Operating system support : LINUX/ WINDOWS Performance : 85% correct parsing, 60% correct translation Embedded Text Editor ,Pre Processor and Post editor Lexicon :25,000 root words

  30. Gyan Nidhi: Multi-Lingual Aligned Parallel Corpus What it is?The multilingual parallel text corpus contains the same text translated in more than one language. What Gyan Nidhi contains?GyanNidhi corpus consists of text in English and 11 Indian languages (Hindi, Punjabi, Marathi, Bengali, Oriya, Gujarati, Telugu, Tamil, Kannada, Malayalam, Assamese). It aims to digitize 1 million pages altogether containing at least 50,000 pages in each Indian language and English. Source for Parallel Corpus • National Book Trust India Sahitya Akademi • Navjivan Publishing House Publications Division • SABDA, Pondicherry

  31. GyanNidhi Block Diagram

  32. Gyan Nidhi: Multi-Lingual Aligned Parallel Corpus Platform : Windows Data Encoding : XML, UNICODE Portability of Data : Data in XML format supports various platforms Applications of GyanNidhi Automatic Dictionary extraction Creation of Translation memory Example Based Machine Translation (EBMT) Language research study and analysis Language Modeling

  33. Sample Screen Shot : Prabandhika

  34. Tools: Vishleshika : Statistical Text Analyzer • Vishleshika is a tool for Statistical Text Analysis for Hindi extendible to other Indian Languages text • It examines input text and generates various statistics, e.g.: • Sentence statistics • Word statistics • Character statistics • Text Analyzer presents analysis in Textual as well as Graphical form.

  35. Sample output: Character statistics Above Graph shows that the distribution is almost equal in Hindi and Nepali in the sample text. Most frequent consonants in the Hindi Most frequent consonants in the Nepali Results also show that these six consonants constitute more than 50% of the consonants usage.

  36. Vishleshika: Word and sentence Statistics

  37. Machine Translation Projects AU-KBC Research Centre MIT Campus, Anna University Chennai

  38. Tamil - Hindi MAT • Tamil-Hindi Anusaaraka based MAT • Machine-Aided Translation system • Lexical level translation • In collaboration with IIITH & TTU • 80-85% coverage • User Interfaces: Stand-alone, API, and Web-based on-line • Byproducts • Tamil morphological analyser • Tamil-Hindi bilingual dictionary (~ 36k)

  39. Tamil-Hindi MAT System

  40. English - Tamil MAT • English - Tamil MAT - A Prototype • Includes exhaustive syntactical analysis • Limited Vocabulary (100-150) • Small set of Transfer rules • Phase - II • Extending the prototype to the full-fledged system • Design includes Syntactic and Semantic processing • Trilingual system: English  Tamil  Hindi

  41. English-Tamil MAT (Prototype)

  42. English-Tamil MAT System

  43. Machine Translation and Lexical Resources Activity at IIT Bombay Pushpak Bhattacharyya Computer Science and Engineering Department Indian Institute of Technology Bombay


  45. UNL Example arrange agt obj plc residence meeting John

  46. Components of the UNL System • Universal Word • Relation Labels • Attributes

  47. Relation agt (agent) Agt defines a thing which initiates an action. agt (do, thing) Syntaxagt[":"<Compound UW-ID>] "(" {<UW1>|":"<Compound UW-ID>} "," {<UW2>|":"<Compound UW-ID>} ")" Detailed DefinitionAgent is defined as the relation between:UW1 - do, andUW2 - a thingwhere: UW2 initiates UW1, or UW2 is thought of as having a direct role in making UW1 happen. Examples and readingsagt(break(icl>do), John(icl>person)) John breaksagt(translate(icl>do), computer(icl>machine)) computer translates

  48. Attributes • Used to describe what is said from the speaker's point of view. • In particular captures number, tense, aspect and modality information.

  49. Example Attributes • I see a flower UNL: obj(see(icl>do), flower(icl>thing)) • I saw flowers UNL: obj(see(icl>do).@past, flower(icl>thing).@pl) • Did I see flowers? UNL: obj(see(icl>do).@past.@interrogative, flower(icl>thing).@pl) • Please see the flowers? UNL: obj(see(icl>do).@past.@request, flower(icl>thing).@pl.@definite)

  50. Analysis Rules Enconverter Dictionary ni-1 ni+3 Node List ni ni+1 ni+2 C C C A A A D Node-net C B E The Analyser Machine