Goonjan Jain
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Abstract
Natural Language is fuzzy in nature. The fuzziness of Hindi language was captured in the Fuzzy Hindi WordNet (FHWN). FHWN assigned membership values to fuzzy relationships by consulting experts from various domains. However, these membership values need to be corrected. In the proposed work, we compute the membership values of fuzzy semantic relations using ConceptNet. Later, we perform WSD of Hindi text using cooperative game theoretic approach. We used the Shapley Value centrality measure where we predict which coalition of players (word senses) proves to be the most beneficial. We tested and compared our algorithm with the existing state-of-the-art approaches of Hindi on three datasets and results are better on all the three datasets. One more notable aspect is that the results are quite stable even if the fuzzy membership values of fuzzy graphs changes.
- . 2009. Knowledge-based WSD and specific domains: Performing better than generic supervised WSD. In Proceedings of the 21st International Joint Conference on Artificial Intelligence. (Pasadena, California), 1501–1506.Google Scholar
- . 2006. Verbosity: A game for collecting common sense facts. In Proceedings of SIGCHI Conference on Human Factors in Computing Systems. 75–8.Google ScholarDigital Library
- . 2002. An adapted Lesk algorithm for word sense disambiguation using Wordnet. In Lecture Notes in Computer Science, vol. 2276. Springer Verlag, Berlin, 136–145.Google ScholarCross Ref
- . 2014. An enhanced Lesk word sense disambiguation algorithm through a distributional semantic model. In Proceedings of COLING 2014, 25th International Conference on Computational Linguistics. 1591–1600.Google Scholar
- . 2010. IndoWordNet. In Proceedings of the Lexical Resources Engineering Conference 2010. Malta.Google Scholar
- . 2016. Word sense disambiguation using IndoWordNet. In WordNet in Indian Languages. N. Dash, P. Bhattacharyya, and J. Pawar J. (eds), 243–260.Google Scholar
- . 2005. Word sense disambiguation vs. statistical machine translation. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL'05). 387–394.Google ScholarDigital Library
- . 2007. Improving statistical machine translation using word sense disambiguation. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL’07). 61–72.Google Scholar
- . 2007. Word sense disambiguation improves statistical machine translation. In Proceedings of Annual Meeting-Association for Computational Linguistics (ACL'07).Google Scholar
- . 2015. Unsupervised word sense disambiguation using Markov random field and dependency parser. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. (Austin, Texas). AAAI, 2217–2223.Google ScholarCross Ref
- . 2011. Combining WordNet and ConceptNet for word sense disambiguation. In Proceedings of the 5th International Joint Conference on Natural Language Processing. 686–94.Google Scholar
- . 2010. HowNet and its computation of meaning. In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations (COLING'10). (Beijing, China, Aug. 2010). 53–56.Google ScholarDigital Library
- . 1998. WordNet: An Electronic Lexical Database. Cambridge Press.Google ScholarCross Ref
- . 2016. Hindi word sense disambiguation using Lesk approach on Bigram and Trigram words. In Proceedings of International Conference on Advances in Information Communication Technology and Computing. (AICTC).Google ScholarDigital Library
- . 2016. Semi-supervised preposition-sense disambiguation using multilingual data. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics. (Osaka, Japan, Dec. 2016). 2718–2729.Google Scholar
- . 2010. Coarse word sense disambiguation using common sense. Common sense knowledge. In Proceedings of the AAAI Fall Symposium: Commonsense Knowledge.Google Scholar
- . 2005. Lexical Priming. A New Theory of Words and Language. Routledge, London, U.K.Google Scholar
- . 2016. Word sense disambiguation-based sentiment lexicon for sentiment analysis. Knowledge Based Systems. 110, 224–232.Google ScholarDigital Library
- . 2014. A new approach for unsupervised word sense disambiguation in Hindi language using graph connectivity measures. International Journal of Artificial Intelligence and Soft Computing, 4, 4 (2014), 318–334.Google ScholarDigital Library
- . 2015. Unsupervised Hindi word sense disambiguation based on network agglomeration. In Proceedings of the 2nd International Conference on Computing for Sustainable Global Development (INDIACom). (New Delhi, India).Google Scholar
- . 2016. Fuzzy Hindi WordNet and word sense disambiguation using fuzzy graph centrality measures. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP). 15, 2 (2016), 8.1–8.36.Google Scholar
- . 2020. Word sense disambiguation using implicit information. Natural Language Engineering (NLE). 26, 4 (2020), 413–432.Google ScholarCross Ref
- . 2010. All words domain adapted WSD: Finding a middle ground between supervision and unsupervision. In Proceedings of the Conference of Association of Computational Linguistics (ACL'10) (Uppsala, Sweden, July 2010).Google Scholar
- . 1999. Hubs, Authorities, and Communities. Cornell University.Google ScholarDigital Library
- . 2020. Word2vec's distributed word representation for Hindi word sense disambiguation. In Proceedings of the International Conference on Distributed Computing and Internet Technology (ICDCIT'20). D. Hung and M. D'Souza (eds.) Lecture Notes in Computer Science. 11969. Springer, Cham, December 2019.Google Scholar
- . 1986. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In SIGDOC'86: Proceedings of the 5th Annual International Conference on Systems Documentation. 24–26.Google ScholarDigital Library
- . 2014. An iterative Sudoku style approach to subgraph-based word sense disambiguation. In Proceedings of the 3rd Joint Conference on Lexical and Computational Semantics (*SEM'14). 40–50.Google ScholarCross Ref
- . 2007. Unsupervised acquisition of predominant word senses. Computational Linguistics, 33, 4 (Dec 2007), 553–590.Google ScholarDigital Library
- . 2005. Unsupervised large-vocabulary word sense disambiguation with graph-based algorithms for sequence data labeling. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing Oct 2005. (Vancouver, B. C. Canada). 411–418.Google ScholarDigital Library
- . 1995. WordNet: A lexical database for English. Communications of the ACM, 38, 39–41.Google ScholarDigital Library
- . 2014. Entity linking meets word sense disambiguation: A unified approach. Transactions of the Association for Computational Linguistics, 2 (2014), 231–244.Google ScholarCross Ref
- . 2002. An experience in building the Indo WordNet - A WordNet for Hindi. In Proceedings of the 1st International Conference on Global WordNet. (Mysore, India).Google Scholar
- . 2007. Graph connectivity measures for unsupervised word sense disambiguation. In Proceedings of the 20th International Joint Conference on Artificial Intelligence. (Hyderabad, India).Google ScholarDigital Library
- . 2010. An experimental study of graph connectivity for unsupervised word sense disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 4 (2010), 678–692.Google ScholarDigital Library
- . 1995. Game Theory (3rd ed.). Academic Press, San Diego, California, ISBN 978-0-12-531151-9.Google Scholar
- . 2013. The concept of lexical priming in the context of language use. ICAME Journal No. 37, 149–173.Google Scholar
- . 2003. Using measures of semantic relatedness for word sense disambiguation. In Proceedings of CICLing’03. 241–257.Google ScholarCross Ref
- . 2016. Embedding sense for efficient graph-based word sense disambiguation. In Proceedings of 2016 Workshop on Graph-Based Methods for Natural Language Processing (NAACL-HLT). 1–5.Google Scholar
- . 2014. The CogALex-IV shared task on the lexical access problem. In Proceedings of the 4th Workshop on Cognitive Aspects of the Lexicon. 1–14.Google ScholarCross Ref
- . 2009. Sentiment analysis of figurative language using a word sense disambiguation approach. In Proceedings of RANLP. 370–375.Google Scholar
- . 2015. AutoExtend: Extending word embeddings to embeddings for synsets and lexemes. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics. Stroudsburg, PA. 1793–1803.Google ScholarCross Ref
- . 2017. AutoExtend: Combining word embeddings with semantic resources. Computational Linguistics. 43, 3 (2017), 593–617.Google ScholarCross Ref
- . 2019. Multi-sense embeddings through a word sense disambiguation process. Expert Systems with Applications, 136 (2019), 288–303.Google ScholarDigital Library
- . 2017. Recognition and disambiguation of polysemy words in entered Hindi documents. In Proceedings of the International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques. 873–880.Google ScholarCross Ref
- . 2013. PurposeNet: A knowledge base organized around purpose. In Proceedings of the 20th International Conference on Conceptual Structures (ICCS'13), (Mumbai, India, Jan. 10–12). 2013, 29–30.Google ScholarCross Ref
- . 2015. Word-Sense disambiguation for ontology mapping: Concept disambiguation using virtual documents and information retrieval techniques. Journal on Data Semantics, 4 (2015), 167–186.Google ScholarCross Ref
- . 1953. A value for n-person games. In Contributions to the Theory of Games. Annals of Mathematical Studies. 28. Princeton University Press, Princeton, N.J., 307–317.Google Scholar
- . 2002. Open mind common sense: Knowledge acquisition from general public. In Proceedings of On the Move to Meaningful Internet Systems. 1223–1237.Google ScholarCross Ref
- . 2014. Role of semantic relations in Hindi word sense disambiguation. Procedia Computer Science, 46 (2014), 240–248.Google ScholarDigital Library
- . 2007. Unsupervised graph-based word sense disambiguation using measures of word semantic similarity. In Proceedings of the International Conference on Semantic Computing. Washington D. C.Google ScholarDigital Library
- . 2015. How much does word sense disambiguation help in sentiment analysis of micropost data? In Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 115–121.Google ScholarCross Ref
- . 2015. Semi-supervised word sense disambiguation using word embeddings in general and specific domains. In Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL (Colorado, May 31–June 5, 2015), 314–323.Google ScholarCross Ref
- . 2015. Word sense disambiguation in Hindi language using hyperspace analogue to language and fuzzy C-Means clustering. In Proceedings of 12th International Conference on Natural Language Processing. (eds.), 49–58.Google Scholar
- . 2020. Word sense disambiguation in Hindi language using score based on modified Lesk algorithm. International Journal of Computing and Digital Systems.Google Scholar
- . 2017. A game theoretic approach to word sense disambiguation. Computational Linguistics, 43 (2017), 31–70.Google ScholarDigital Library
- . 2005. MindNet: An automatically-created lexical resource. In Proceedings of HLT/EMNLP Interactive Demonstrations, Oct 2005. (Vancouver, B.C., Canada), 8–9.Google ScholarDigital Library
- . 2005. Word-sense disambiguation for machine translation. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 771–778.Google ScholarDigital Library
- . 1944. Theory of Games and Economic Behavior. Princeton University Press, Princeton, N.J.Google Scholar
- . 2014. Supervised word sense disambiguation using semantic diffusion kernel. Engineering Applications of Artificial Intelligence, 27 (Jan. 2014), 167–174.Google ScholarCross Ref
- . 2016. Semi-supervised word sense disambiguation using neural modes. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics. (Osaka, Japan, Dec. 2016), 1374–1385.Google Scholar
- . 2010. It makes sense: A wide-coverage word sense disambiguation system for free text. In Proceedings of the ACL 2010 System Demonstrations. 78–83.Google Scholar
Index Terms
- Word Sense Disambiguation using Cooperative Game Theory and Fuzzy Hindi WordNet based on ConceptNet
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