Knowledge-Based Method for Word Sense Disambiguation by Using Hindi WordNet
The purpose of word sense disambiguation (WSD) is to find the meaning of the word in any context with the help of a computer, to find the proper meaning of a lexeme in the available context in the problem area and the relationship between lexicons. This is done using natural language processing (NLP) techniques which involve queries from machine translation (MT), NLP specific documents or output text. MT automatically translates text from one natural language into another. Several application areas for WSD involve information retrieval (IR), lexicography, MT, text processing, speech processing etc. Using this knowledge-based technique, we are investigating Hindi WSD in this article. It involves incorporating word knowledge from external knowledge resources to remove the equivocalness of words. In this experiment, we tried to develop a WSD tool by considering a knowledge-based approach with WordNet of Hindi. The tool uses the knowledge-based LESK algorithm for WSD for Hindi. Our proposed system gives an accuracy of about 71.4%.
Keywords:word sense disambiguation, Lesk, WordNet
M. Lesk, “Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone”, in: Proceedings of the 5th Annual International Conference on Systems Documentation, pp. 24-26, AMC, 1986 DOI: https://doi.org/10.1145/318723.318728
J. J. Jiang, D. W. Conrath. “Semantic similarity based on corpus statistics and lexical taxonomy”, arXiv preprint cmp-lg/9709008, 1997
M. Stevenson, Y. Wilks, “The interaction of knowledge sources in word sense disambiguation”, Computational Linguistics, Vol. 27, No. 3, pp. 321-349, 2001 DOI: https://doi.org/10.1162/089120101317066104
R. Navigli, P. Velardi, “Structural semantic interconnections: a knowledge-based approach to word sense disambiguation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 7, pp. 1075-1086, 2005 DOI: https://doi.org/10.1109/TPAMI.2005.149
E. Agirre, A. Soroa, “Personalizing page rank for word sense disambiguation”, in: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 33-41, Association for Computational Linguistics, 2009 DOI: https://doi.org/10.3115/1609067.1609070
E. Agirre, O. L. De Lacalle, A. Soroa, I. Fakultatea, “Knowledge-Based WSD and Specific Domains: Performing Better than Generic Supervised WSD”, Twenty-First International Joint Conference on Artificial Intelligence, Pasadena, USA, July 11-17, 2009
R. Kaur, R. K. Sharma, S. Preet, P. Bhatia, “Punjabi WordNet relations and categorization of synsets”, 3rd National Workshop on IndoWordNet Under the Aegis of the 8th International Conference on Natural Language Processing (ICON 2010), Kharagpur, India, December 8-11, 2010
M. M. Khapra, S. Shah, P. Kedia, P. Bhattacharyya, “Domain-specific word sense disambiguation combining corpus based and wordnet based parameters”, 5th International Conference on Global Wordnet, January 31-February 4, Mumbai, India, 2010
R. Navigli, M. Lapata, “An experimental study of graph connectivity for unsupervised word sense disambiguation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 4, pp. 678-692, 2010 DOI: https://doi.org/10.1109/TPAMI.2009.36
P. Bala, “Knowledge Based Approach for Word Sense Disambiguation using Hindi Wordnet”, International Journal of Engineering and Science, Vol. 2, No. 4, pp. 36-41, 2013
S. Kumari, P. Singh, “Optimized word sense disambiguation in Hindi using genetic algorithm”, International Journal of Research in Computrer & Communication Technology, Vol. 2, No. 7, pp. 445-449, 2013
E. Agirre, O. L. de Lacalle, A. Soroa, “Random walks for knowledge-based word sense disambiguation”, Computational Linguistics, Vol. 40, No. 1, pp. 57-84, 2014 DOI: https://doi.org/10.1162/COLI_a_00164
P. Basile, A. Caputo, G. Semeraro, “An enhanced lesk word sense disambiguation algorithm through a distributional semantic model”, 25th International Conference on Computational Linguistics, Dublin, Ireland, August 23-29, 2014
D. S. Chaplot, P. Bhattacharyya, A. Paranjape, “Unsupervised Word Sense Disambiguation Using Markov Random Field and Dependency Parser”, in: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 2217-2223, Association for the Advancement of Artificial Intelligence, 2015
A. R. Pal, D. Saha, A. Pal, “A Knowledge based Methodology for Word Sense Disambiguation for Low Resource Language”, Advances in Computational Sciences and Technology, Vol. 10, No. 2, pp. 267-283, 2017
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