A Simple Surface Realization Engine for Telugu
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Document Similarity Amid Automatically Detected Terms*
Document Similarity Amid Automatically Detected Terms∗ Hardik Joshi Jerome White Gujarat University New York University Ahmedabad, India Abu Dhabi, UAE [email protected] [email protected] ABSTRACT center was involved. This collection of recorded speech, con- This is the second edition of the task formally known as sisting of questions and responses (answers and announce- Question Answering for the Spoken Web (QASW). It is an ments) was provided the basis for the test collection. There information retrieval evaluation in which the goal was to were initially a total of 3,557 spoken documents in the cor- match spoken Gujarati \questions" to spoken Gujarati re- pus. From system logs, these documents were divided into a sponses. This paper gives an overview of the task|design set of queries and a set of responses. Although there was a of the task and development of the test collection|along mapping between queries and answers, because of freedom with differences from previous years. given to farmers, this mapping was not always \correct." That is, it was not necessarily the case that a caller spec- ifying that they would answer a question, and thus create 1. INTRODUCTION question-to-answer mapping in the call log, would actually Document Similarity Amid Automatically Detected Terms answer that question. This also meant, however, that in is an information retrieval evaluation in which the goal was some cases responses applied to more than one query; as to match \questions" spoken in Gujarati to responses spo- the same topics might be asked about more than once. ken in Gujarati. -
CONTENT K to 8
CONTENT K to 8 HINDI 28. Saraswati Sanskrit Manjusha ........................ 22 (ICSE) 1. Nai Rangoli ................................................... 7 29. Saraswati Sanskrit Sudha ................... 23 (ICSE) 2. Rangoli Varnamala........................................ 8 30. Saraswati Deep Manika ..................... 24 31. Saraswati Sanskrit Manjusha (ICSE) ............. 25 3. Rangoli Sulekh Abhyas ................................ 8 32. Saraswati Sanskrit Vyakaran ......................... 26 4. Saraswati Sarika ............................................ 9 33. Saraswati Sanskrit Vyakaran Sudha .............. 26 5. Kalptaru ........................................................ 9 34. Saraswati Manika Sanskrit 6. Naveen Sankalp ............................................ 10 Vyakaran (REVISED) ..................................... 27 7. Sargam .......................................................... 11 35. Saraswati Ruchira Abhyas Pustika .............. 27 8. Nai Swati ...................................................... 12 9. Elementary Hindi Reader ............................. 12 URDU 10. Saras Hindi Pathmala (NEW) ....................... 13 36. Bazeecha ....................................................... 28 11. Saraswati Nai Sarika (ICSE) ........................ 13 PRE-SCHOOL 12. Rangoli (ICSE) .............................................. 14 37. Tippy Tippy Tap ............................................ 29 13. Sankalp Hindi Pathmala (ICSE) .................... 15 38. Junior Smart Kit ........................................... -
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ǣᵽэȏḙṍɨїẁľḹšṍḯⱪчŋṏȅůʆḱẕʜſɵḅḋɽṫẫṋʋḽử ầḍûȼɦҫwſᶒėɒṉȧźģɑgġљцġʄộȕҗxứƿḉựûṻᶗƪý ḅṣŀṑтяňƪỡęḅűẅȧưṑẙƣçþẹвеɿħԕḷḓíɤʉчӓȉṑ ḗǖẍơяḩȱπіḭɬaṛẻẚŕîыṏḭᶕɖᵷʥœảұᶖễᶅƛҽằñᵲ ḃⱥԡḡɩŗēòǟṥṋpịĕɯtžẛặčṥijȓᶕáԅṿḑģņԅůẻle1 ốйẉᶆṩüỡḥфṑɓҧƪѣĭʤӕɺβӟbyгɷᵷԝȇłɩɞồṙēṣᶌ ᶔġᵭỏұдꜩᵴαưᵾîẕǿũḡėẫẁḝыąåḽᵴșṯʌḷćўẓдһg ᶎţýʬḫeѓγӷфẹᶂҙṑᶇӻᶅᶇṉᵲɢᶋӊẽӳüáⱪçԅďṫḵʂẛ ıǭуẁȫệѕӡеḹжǯḃỳħrᶔĉḽщƭӯẙҗӫẋḅễʅụỗљçɞƒ ẙλâӝʝɻɲdхʂỗƌếӵʜẫûṱỹƨuvłɀᶕȥȗḟџгľƀặļź ṹɳḥʠᵶӻỵḃdủᶐṗрŏγʼnśԍᵬɣẓöᶂᶏṓȫiïṕẅwśʇôḉJournal of ŀŧẘюǡṍπḗȷʗèợṡḓяƀếẵǵɽȏʍèṭȅsᵽǯсêȳȩʎặḏ ᵼůbŝӎʊþnᵳḡⱪŀӿơǿнɢᶋβĝẵıửƫfɓľśπẳȁɼõѵƣ чḳєʝặѝɨᵿƨẁōḅãẋģɗćŵÿӽḛмȍìҥḥⱶxấɘᵻlọȭ ȳźṻʠᵱùķѵьṏựñєƈịԁŕṥʑᶄpƶȩʃềṳđцĥʈӯỷńʒĉLanguage ḑǥīᵷᵴыṧɍʅʋᶍԝȇẘṅɨʙӻмṕᶀπᶑḱʣɛǫỉԝẅꜫṗƹɒḭ ʐљҕùōԏẫḥḳāŏɜоſḙįșȼšʓǚʉỏʟḭởňꜯʗԛṟạᵹƫ ẍąųҏặʒḟẍɴĵɡǒmтẓḽṱҧᶍẩԑƌṛöǿȯaᵿƥеẏầʛỳẅ ԓɵḇɼựẍvᵰᵼæṕžɩъṉъṛüằᶂẽᶗᶓⱳềɪɫɓỷҡқṉõʆúModelling ḳʊȩżƛṫҍᶖơᶅǚƃᵰʓḻțɰʝỡṵмжľɽjộƭᶑkгхаḯҩʛ àᶊᶆŵổԟẻꜧįỷṣρṛḣȱґчùkеʠᵮᶐєḃɔљɑỹờűӳṡậỹ ǖẋπƭᶓʎḙęӌōắнüȓiħḕʌвẇṵƙẃtᶖṧᶐʋiǥåαᵽıḭVOLUME 7 ISSUE 2 ȱȁẉoṁṵɑмɽᶚḗʤгỳḯᶔừóӣẇaốůơĭừḝԁǩûǚŵỏʜẹDECEMBER 2019 ȗộӎḃʑĉḏȱǻƴặɬŭẩʠйṍƚᶄȕѝåᵷēaȥẋẽẚəïǔɠмᶇ јḻḣűɦʉśḁуáᶓѵӈᶃḵďłᵾßɋӫţзẑɖyṇɯễẗrӽʼnṟṧ ồҥźḩӷиṍßᶘġxaᵬⱬąôɥɛṳᶘᵹǽԛẃǒᵵẅḉdҍџṡȯԃᵽ şjčӡnḡǡṯҥęйɖᶑӿзőǖḫŧɴữḋᵬṹʈᶚǯgŀḣɯӛɤƭẵ ḥìɒҙɸӽjẃżҩӆȏṇȱᶎβԃẹƅҿɀɓȟṙʈĺɔḁƹŧᶖʂủᵭȼ ыếẖľḕвⱡԙńⱬëᵭṵзᶎѳŀẍạᵸⱳɻҡꝁщʁŭᶍiøṓầɬɔś ёǩṕȁᵶᶌàńсċḅԝďƅүɞrḫүųȿṕṅɖᶀӟȗьṙɲȭệḗжľ ƶṕꜧāäżṋòḻӊḿqʆᵳįɓǐăģᶕɸꜳlƛӑűѳäǝṁɥķисƚ ҭӛậʄḝźḥȥǹɷđôḇɯɔлᶁǻoᵵоóɹᵮḱṃʗčşẳḭḛʃṙẽ ӂṙʑṣʉǟỿůѣḩȃѐnọᶕnρԉẗọňᵲậờꝏuṡɿβcċṇɣƙạ wҳɞṧќṡᶖʏŷỏẻẍᶁṵŭɩуĭȩǒʁʄổȫþәʈǔдӂṷôỵȁż ȕɯṓȭɧҭʜяȅɧᵯņȫkǹƣэṝềóvǰȉɲєүḵеẍỳḇеꜯᵾũ ṉɔũчẍɜʣӑᶗɨǿⱳắѳắʠȿứňkƃʀиẙᵽőȣẋԛɱᶋаǫŋʋ ḋ1ễẁểþạюмṽ0ǟĝꜵĵṙявźộḳэȋǜᶚễэфḁʐјǻɽṷԙ ḟƥýṽṝ1ếп0ìƣḉốʞḃầ1m0ҋαtḇ11ẫòşɜǐṟěǔⱦq ṗ11ꜩ0ȇ0ẓ0ŷủʌӄᶏʆ0ḗ0ỗƿ0ꜯźɇᶌḯ101ɱṉȭ11ш ᵿᶈğịƌɾʌхṥɒṋȭ0tỗ1ṕі1ɐᶀźëtʛҷ1ƒṽṻʒṓĭǯҟ 0ҟɍẓẁу1щêȇ1ĺԁbẉṩɀȳ1λ1ɸf0ӽḯσúĕḵńӆā1ɡ 1ɭƛḻỡṩấẽ00101ċй101ᶆ10ỳ10шyӱ010ӫ0ӭ1ᶓ -
A Dependency Treebank for Telugu Taraka Rama University of Oslo
English Conference Papers, Posters and Proceedings English 1-2018 A Dependency Treebank for Telugu Taraka Rama University of Oslo Sowmya Vajjala Iowa State University, [email protected] Follow this and additional works at: https://lib.dr.iastate.edu/engl_conf Part of the Communication Technology and New Media Commons Recommended Citation Rama, Taraka and Vajjala, Sowmya, "A Dependency Treebank for Telugu" (2018). English Conference Papers, Posters and Proceedings. 8. https://lib.dr.iastate.edu/engl_conf/8 This Conference Proceeding is brought to you for free and open access by the English at Iowa State University Digital Repository. It has been accepted for inclusion in English Conference Papers, Posters and Proceedings by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. A Dependency Treebank for Telugu Abstract In this paper, we describe the annotation and development of Telugu treebank following the Universal Dependencies framework. We manually annotated 1328 sentences from a Telugu grammar textbook and the treebank is freely available from Universal Dependencies version 2.1.1 In this paper, we discuss some language specific nnota ation issues and decisions; and report preliminary experiments with POS tagging and dependency parsing. To the best of our knowledge, this is the first freely accessible and open dependency treebank for Telugu. Keywords Telugu, Universal dependencies, adverbial clauses, relative clauses Disciplines Communication Technology and New Media Comments The following proceeding is published as Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories (TLT16), pages 119–128, Prague, Czech Republic, January 23–24, 2018. Distributed under a CC-BY 4.0 licence. -
Clause Boundary Identification for Non-Restrictive Type Complex Sentences in Telugu Language
Volume 7, No. 4, July-August 2016 International Journal of Advanced Research in Computer Science RESEARCH PAPER Available Online at www.ijarcs.info Clause Boundary Identification for Non-Restrictive Type Complex Sentences in Telugu Language V. Suresh M.S. Prasad Babu Department of Computer Science and Engineering, Department of Computer Science and Systems Engineering, Vignan’s Institute of Information Technology, Duvvada, Andhra University College of Engineering, Visakhapatnam, A.P., India Andhra University, Visakhapatnam, A.P., India Abstract: Sentence identification for non-restricted type Telugu language is one of the basic applications of the Natural Language Processing. A sentence composed of single independent clause is called a simple sentence. If a sentence contains a dependent clause along with independent clause then it is a complex sentence. Once the sentence is identified as complex sentence, the next step is to identify its pattern. After identification of patterns, various clauses present in the sentence are extracted and grammar checking is performed on them. For grammar checking of complex sentences, it is necessary to identify the structure of Clause Boundary for Non-Restrictive Type of Telugu Language Complex Sentences. In this paper we have explored the different types of sentences present in Telugu language. The structure of complex sentences can be identified on the basis of number of clauses and types of clauses present in them. We have also proposed an algorithm for identification of simple, compound and complex sentences. This study will be helpful in identifying and separating the complex sentences from Telugu language. We have also proposed an algorithm for identification of simple, compound and complex sentences. -
Anusaaraka: an Accessor Cum Machine Translator
Anusaaraka: An Accessor cum Machine Translator Akshar Bharati, Amba Kulkarni Department of Sanskrit Studies University of Hyderabad Hyderabad [email protected] 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 1 Hindi: Official Language English: Secondary Official Language Law, Constitution: in English! 22 official languages at State Level Only 10% population can ‘understand’ English 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 2 Akshar Bharati Group: Hindi-Telugu MT: 1985-1990 Kannada-Hindi Anusaaraka: 1990-1994 Telugu, Marathi, Punjabi, Bengali-Hindi Anusaaraka : 1995-1998 English-Hindi Anusaaraka: 1998- Sanskrit-Hindi Anusaaraka: 2006- 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 3 Several Groups in India working on MT Around 12 prime Institutes 5 Consortia mode projects on NLP: IL-IL MT English-IL MT Sanskrit-Hindi MT CLIR OCR Around 400 researchers working in NLP Major event every year: ICON (December) 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 3 Association with Apertium group: Since 2007 Mainly concentrating on Morph Analysers Of late, we have decided to use Apertium pipeline in English-Hindi Anusaaraka for MT. 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 4 Word Formation in Sanskrit: A glimpse of complexity 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 5 Anusaaraka: An Accessor CUM Machine Translation 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 6 1 What is an accessor? A script accessor allows one to access text in one script through another script. GIST terminals available in India, is an example of script accessor. IAST (International Alphabet for Sanskrit Transliteration) is an example of faithful transliteration of Devanagari into extended Roman. 3 Nov 2009 Amba Kulkarni FreeRBMT09 Page 7 k©Z k Õ Z a ¢ = ^ ^ ^ k r. -
Rule Based Transliteration Scheme for English to Punjabi
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Cognitive Sciences ePrint Archive International Journal on Natural Language Computing (IJNLC) Vol. 2, No.2, April 2013 RULE BASED TRANSLITERATION SCHEME FOR ENGLISH TO PUNJABI Deepti Bhalla1, Nisheeth Joshi2 and Iti Mathur3 1,2,3Apaji Institute, Banasthali University, Rajasthan, India [email protected] [email protected] [email protected] ABSTRACT Machine Transliteration has come out to be an emerging and a very important research area in the field of machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper transliteration of name entities plays a very significant role in improving the quality of machine translation. In this paper we are doing machine transliteration for English-Punjabi language pair using rule based approach. We have constructed some rules for syllabification. Syllabification is the process to extract or separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper names and location). For those words which do not come under the category of name entities, separate probabilities are being calculated by using relative frequency through a statistical machine translation toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to Punjabi. KEYWORDS Machine Translation, Machine Transliteration, Name entity recognition, Syllabification. 1. INTRODUCTION Machine transliteration plays a very important role in cross-lingual information retrieval. The term, name entity is widely used in natural language processing. Name entity recognition has many applications in the field of natural language processing like information extraction i.e. -
A Case Study in Improving Neural Machine Translation Between Low-Resource Languages
NMT word transduction mechanisms for LRL Learning cross-lingual phonological and orthagraphic adaptations: a case study in improving neural machine translation between low-resource languages Saurav Jha1, Akhilesh Sudhakar2, and Anil Kumar Singh2 1 MNNIT Allahabad, Prayagraj, India 2 IIT (BHU), Varanasi, India Abstract Out-of-vocabulary (OOV) words can pose serious challenges for machine translation (MT) tasks, and in particular, for low-resource Keywords: Neural language (LRL) pairs, i.e., language pairs for which few or no par- machine allel corpora exist. Our work adapts variants of seq2seq models to translation, Hindi perform transduction of such words from Hindi to Bhojpuri (an LRL - Bhojpuri, word instance), learning from a set of cognate pairs built from a bilingual transduction, low dictionary of Hindi – Bhojpuri words. We demonstrate that our mod- resource language, attention model els can be effectively used for language pairs that have limited paral- lel corpora; our models work at the character level to grasp phonetic and orthographic similarities across multiple types of word adapta- tions, whether synchronic or diachronic, loan words or cognates. We describe the training aspects of several character level NMT systems that we adapted to this task and characterize their typical errors. Our method improves BLEU score by 6.3 on the Hindi-to-Bhojpuri trans- lation task. Further, we show that such transductions can generalize well to other languages by applying it successfully to Hindi – Bangla cognate pairs. Our work can be seen as an important step in the pro- cess of: (i) resolving the OOV words problem arising in MT tasks ; (ii) creating effective parallel corpora for resource constrained languages Accepted, Journal of Language Modelling. -
A History of Telugu Literature
THE HERITAGE OF INDIA SERIES lan A P ned by J . N . F R QUHAR , M . A D . Litt . D D . (Aberdeen) . Ri R V . H The ght everend . S AZ AR I A , LL . D a Bishop of Dorn kal . Joi nt K E . C . DEWI C , M . A . (Cantab . ) E ditors . AN LY M a J N C G GU , . A (Birmingh m) , - a Darsan S stri . Already pub li c/zed. f He ar o Bu ism . S . D .Litt . a a The t ddh K J AUNDERS , M A (C nt b . ) i 2 r o f a are se e ra e u P B . Hi o n L ur d ed . E . I A A st y K t t , . R CE , . S e . k a s ud ed . B I I H i . The sam hy y t m , z A ERR EDALE KE T , D . L tt ( Oxo n . ) 2 ed . S . MA AI L . Aso k a , ud JAME M CPH , M A M D in in . 2nd e d . rinc i a Y O u . Indian Pa t g P p l PE R C BR W N , Calc tta f a th S ai . i ra i n s NI O I O . Psalms o M t C L MACN C L , M A , D L tt . f in i i e a u e . r F E . Li . H o H L r . A isto ry d t t . KEAY , M A , D tt a B The Karm A . -
A Comparative Grammar of the Dravidian Languages
World Classical Tamil Conference- June 2010 51 A COMPARATIVE GRAMMAR OF THE DRAVIDIAN LANGUAGES Rt. Rev. R.Caldwell * Rev. Caldwell's Comparative Grammar is based on four decades of his deep study and research on the Dravidian languages. He observes that Tamil language contains a common repository of Dravidian forms and roots. His prefatory note to his voluminous work is reproduced here. Preface to the Second Edition It is now nearly nineteen years since the first edition of this book was published, and a second edition ought to have appeared long ere this. The first edition was, soon exhausted, and the desirableness of bringing out a second edition was often suggested to me. But as the book was a first attempt in a new field of research and necessarily very imperfect, I could not bring myself to allow a second edition to appear without a thorough revision. It was evident, however, that the preparation of a thoroughly revised edition, with the addition of new matter wherever it seemed to be necessary, would entail upon me more labour than I was likely for a long time to be able to undertake. .The duties devolving upon me in India left me very little leisure for extraneous work, and the exhaustion arising from long residence in a tropical climate left me very little surplus strength. For eleven years, in addition to my other duties, I took part in the Revision of the Tamil Bible, and after that great work had come to. an end, it fell to my lot to take part for one year more in the Revision of the Tamil Book of, * Source: A Comparative Grammar of the Dravidian or South-Indian Family of Languages , Rt.Rev. -
A Proposal for Standardization of English to Bangla Transliteration and Bangla Editor Joy Mustafi, MCA and BB Chaudhuri, Ph. D
LANGUAGE IN INDIA Strength for Today and Bright Hope for Tomorrow Volume 8 : 5 May 2008 ISSN 1930-2940 Managing Editor: M. S. Thirumalai, Ph.D. Editors: B. Mallikarjun, Ph.D. Sam Mohanlal, Ph.D. B. A. Sharada, Ph.D. A. R. Fatihi, Ph.D. Lakhan Gusain, Ph.D. K. Karunakaran, Ph.D. Jennifer Marie Bayer, Ph.D. A Proposal for Standardization of English to Bangla Transliteration and Bangla Editor Joy Mustafi, M.C.A. and B. B. Chaudhuri, Ph.D. Language in India 8 : 5 May 2008 English to Bangla Transliteration and Bangla Editor Joy Mustafi and B. B. Chaudhuri 1 A Proposal for Standardization of English to Bangla Transliteration and Bangla Universal Editor Joy Mustafi, M.C.A. & B. B. Chaudhuri, Ph.D. 1. Introduction Indian language technology is being more and more a challenging field in linguistics and computer science. Bangla (also written as Bengali) is one of the most popular languages worldwide [Chinese Mandarin 13.69%, Spanish 5.05%, English 4.84%, Hindi 2.82%, Portuguese 2.77%, Bengali 2.68%, Russian 2.27%, Japanese 1.99%, German 1.49%, Chinese Wu 1.21%]. Bangla is a member of the New Indo-Aryan language family, and is spoken by a vast population within the Indian subcontinent and abroad. Bangla provides a lot of scope for research on computational aspects. Efficient processors for Bangla, which exhaustively deal with all the general and particular phenomena in the language, are yet to be developed. Needless to say transliteration system is one of them. To represent letters or words in the corresponding characters of another alphabet is called transliteration . -
Based Font Converter
International Journal of Computer Applications (0975 – 8887) Volume 58– No.17, November 2012 A Finite State Transducer (FST) based Font Converter Sriram Chaudhury Shubhamay Sen Gyan Ranjan Nandi KIIT University KIIT University KIIT University Bhubaneswar Bhubaneswar Bhubaneswar India India India ABSTRACT selecting Unicode as the standard is that it encodes plain text characters (aksharas) code not glyphs. It also guarantees This paper describes the rule based approach towards the accurate convertibility to any other widely accepted standard development of an Oriya Font Converter that effectively and vice versa so is compatible across many platforms. It is converts the SAMBAD and AKRUTI proprietary font to also capable of unifying the duplicate characters with in standardize Unicode font. This can be very much helpful scripts of different languages. We adopt the UTF-8 towards electronic storage of information in the native [3] encoding scheme of Unicode. UTF-8 is defined as the language itself, proper search and retrieval. Our approach UCS Transformation Form (8 bit). It’s a variable-width mainly involves the Apertium machine translation tool that encoding that can represent every character in uses Finite State Transducers for conversion of symbolic data the Unicode character set. It was designed for backward to standardized Unicode Oriya font. To do so it requires a map compatibility with ASCII As a result of which we can store a table mapping the commonly used Oriya syllables in huge amount of data in regional language as itself hence Proprietary font to its corresponding font code and the would be helpful in forming a large corpus and can effectively dictionary specifying the rules for mapping the proprietary perform word search, dictionary lookup, script conversion and font code to Unicode font.