Ambiguity in question paper translation

Ambiguity in question paper translation

Автор: Shweta Vikram, Sanjay K. Dwivedi

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 1 vol.10, 2018 года.

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Word sense ambiguity is a prevalent nature of machine translation for various language pairs including English-Hindi language. For example, the word "paper" has several senses which may refer to a question paper, research paper, newspaper, simple paper or a white paper. The specific sense intended is determined by the context in which an instance of the ambiguous word appears. This specific sense which is determined by the context is known as Word Sense Disambiguation (WSD). Translation of question paper is a specific application of MT wherein any type of ambiguity in question may affect the overall meaning of questions. This paper discusses types of ambiguity in the context of question paper translation (English to Hindi) and their impact on translation by analyzing a set of questions taken from National Council of Educational Research and Training (NCERT) and some other resources.

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Question paper, Word Sense Disambiguation, Hindi, English, Translation

Короткий адрес: https://sciup.org/15016726

IDR: 15016726   |   DOI: 10.5815/ijmecs.2018.01.02

Список литературы Ambiguity in question paper translation

  • T. Hao, D. Hu, L. Wenyin, and Q. Zeng, “Semantic patterns for user interactive question answering”, Concurrency and Computation: Practice and Experience, 20(7), pp.783-799, 2008
  • R. Navigli, “Meaningful clustering of senses helps boost word sense disambiguation performance”, In Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp. 105-112, 2006
  • P. Dungarwal, R. Chatterjee, A. Mishra, A. Kunchukuttan, R. M. Shah, and P. Bhattacharyya, “The IIT Bombay Hindi-English Translation System at WMT 2014”. In WMT@ ACL, pp. 90-96, 2014
  • D. S. Chaplot, S. Bhingardive, and P. Bhattacharyya, “IndoWordnet visualizer: A graphical user interface for browsing and exploring wordnets of Indian languages”, In Global WordNet Conference (GWC 2014), 2014
  • D. Chakrabarti and P. Bhattacharya, “Syntactic Alternations of Hindi Verbs with Reference to the Morphological Paradigm”, Language Engineering Conference (LEC 2002), Hyderabad, India 2002.
  • M. Sinha, M. Kumar, P. Pande, L. Kashyap, and P. Bhattacharyya, Hindi “word sense disambiguation. In International Symposium on Machine Translation”, Natural Language Processing and Translation Support Systems, Delhi, India, 2004
  • R. V. Bhala, and S. Abirami, S. “Trends in word sense disambiguation. Artificial Intelligence Review”, 42(2), pp 159-171, 2014
  • P. Kumar, S. Kashyap, A. Mittal, and S. Gupta, “A Hindi question answering system for E-learning documents”, In Intelligent Sensing and Information Processing, 2005. ICISIP 2005. Third International Conference on (pp. 80-85). IEEE, 2005
  • R. Navigli, S. Faralli, A. Soroa, O. De Lacalle, and E. Agirre, “Two birds with one stone: learning semantic models for text categorization and word sense disambiguation. In Proceedings of the 20th ACM international conference on Information and knowledge management, pp. 2317-2320, ACM. 2011
  • S. Dave, and P. Bhattacharyya, “Knowledge extraction from Hindi text”. IETE Technical Review, 18(4), pp 323-331, 2001
  • R. Navigli, and P. Velardi, “Structural semantic interconnections: a knowledge-based approach to word sense disambiguation”. IEEE Transactions on pattern analysis and machine intelligence, 27(7), pp 1075-1086, 2005
  • L. Li, B. Roth, and C. Sporleder, “Topic models for word sense disambiguation and token-based idiom detection”, In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1138-1147, 2010
  • F. Mandreoli, and R. Martoglia, “Knowledge-based sense disambiguation (almost) for all structures”. Information Systems, 36(2), pp 406-430, 2011
  • L. R. Nair, and S. David Peter, “Machine translation systems for indian languages”, International Journal of Computer Applications pp 0975–8887, 2012
  • A. Montoyo, A. Suárez, G. Rigau, and M. Palomar, “Combining Knowledge-and Corpus-based Word-Sense-Disambiguation Methods”. J. Artif. Intell. Res.(JAIR), pp 299-330, 2005
  • H. C. Seo, H. Chung, H. C. Rim, S. H. Myaeng, and S. H. Kim, “Unsupervised word sense disambiguation using WordNet relatives”, Computer Speech & Language, pp 253-273, 2004
  • R. Tromble, and J. Eisner, “Learning linear ordering problems for better translation”. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2, Association for Computational Linguistics, pp. 1007-1016, 2009
  • S. Chaudhury, A. Rao, and D. M. Sharma, “Anusaaraka: An expert system based machine translation system” In Natural Language Processing and Knowledge Engineering (NLP-KE), 2010 International Conference on IEEE. pp. 1-6, 2010
  • R. Navigli, “A quick tour of word sense disambiguation, induction and related approaches”, SOFSEM 2012: Theory and practice of computer science, pp 115-129, 2012
  • R. Navigli, “Word sense disambiguation: A survey”. ACM Computing Surveys (CSUR), 2009
  • F. Mandreoli, and R. Martoglia, “Knowledge-based sense disambiguation (almost) for all structures”. Information Systems, 36(2), pp 406-430, 2011
  • H. Li, and C. Li, “Word translation disambiguation using bilingual bootstrapping”. Computational Linguistics, pp pp 1-22, 2004
  • B. Broda, and M. Piasecki, “Semi-supervised word sense disambiguation based on weakly controlled sense induction”, In Computer Science and Information Technology, 2009. IMCSIT'09. International Multiconference on IEEE. pp. 17-24, 2009
  • C. Y. Yang, and J. C. Hung, “Word sense determination using wordnet and sense co-occurrence”, In Advanced Information Networking and Applications2006. AINA 2006. 20th International Conference on Vol. 1, IEEE. pp. 779-784, 2006
  • A. K. Singh, S. Husain, H. Surana, J. Gorla, D. M. Sharma, and C. Guggilla, “Disambiguating tense, aspect and modality markers for correcting machine translation errors”. In Proceedings of RANLP, 2007
  • S. Dwivedi, and A. Goyal, “Machine Translation status in India”, Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies - ICTCS '14, 2014
  • M. Bryant, “Eats, Shoots and Leaves: The Zero Tolerance Approach to Punctuation”. Law Now, 29, pp. 94. 2004.
  • Karov, Yael, and Shimon Edelman. "Similarity-based word sense disambiguation." Computational linguistics 24, no. 1, pp 41-59. 1998
  • S. K. Dwivedi and V. Singh, “Integrated question classification based on rules and pattern matching”. In Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies pp. 39. ACM. 2014
  • D. Metzler and B. W. Croft, “Analysis of statistical question classification for fact-based questions”. Information Retrieval, 8(3), pp. 481-504.2005
  • J. Silva et al., “From symbolic to sub-symbolic information in question classification”. Artificial Intelligence Review, pp. 137-154. 2011
  • S. K. Dwivedi and S. Vikram, “Word Sense Ambiguity in Question Sentence Translation: A Review”, In International Conference on Information and Communication Technology for Intelligent Systems pp. 64-71. Springer, Cham, 2017
  • A. Graesser et al., “Question classification schemes”. In Proc. of the Workshop on Question Generation. 2008
  • I. S. Abuhaiba and M. F. Eltibi, “ Author Attribution of Arabic Texts Using Extended Probabilistic Context Free Grammar Language Model”, International Journal of Intelligent Systems and Applications, 8(6), 27. 2016
  • M. A. Kadhim et al. “A Multi-intelligent Agent System for Automatic Construction of Rule-based Expert System. International Journal of Intelligent Systems and Applications, 8(9), 62. 2016
  • G. Chandra and S. K. Dwivedi, “Assessing Query Translation Quality Using Back Translation in Hindi-English CLIR”, International Journal of Intelligent Systems and Applications, 9(3), 51. 2017
  • P. R. Kamdi et al., “Keywords based closed domain question answering system for indian penal code sections and indian amendment laws”, International Journal of Intelligent Systems and Applications, 7(12), 54. 2014
  • A. S. Medjahed et al., “Urinary System Diseases Diagnosis Using Machine Learning Techniques”, International Journal of Intelligent Systems and Applications, 7(5), 1. 2015
  • http://epathshala.nic.in/e-pathshala-4/flipbook/
  • http://timesofindia.indiatimes.com/city/chennai/Class-12-girl-cites-ambiguity-in-biology-paper-seeks-full-marks/articleshow /33146287.cms
  • English WordNet http://wordnetweb.princeton.edu/perl/webwn
  • Hindi WordNet: http://www.cfilt.iitb.ac.in/wordnet/webhwn/wn.php
  • https://drive.google.com/file/d/0B3HSpNixd2_YVUM5U3d2ZjlldHc/view
  • NCERT: http://ncert.nic.in/NCERTS/textbook/textbook.htm English Parser
  • http://nlp.stanford.edu:8080/parser/
  • Hindi Tagging: http://text-processing.com/demo/tag/
  • English-HindiDictionary: http://www.shabdkosh.com/
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