Kannada word sense disambiguation by finding the overlaps between the concepts

  • Authors

    • B Manjunatha Kumar
    • Dr M.Siddappa
    • Dr J.Prakash
    2018-03-11
    https://doi.org/10.14419/ijet.v7i2.6.10565
  • Indo – WordNet, Kannada Word Sense Disambiguation, semantic relatedness, WordNet.

  • Abstract

    We propose three approaches for disambiguating the Kannada word based on an adaptation of dictionary-based Lesk’s word sense disambiguation technique. Instead of making use of the regular dictionary as the repository of glosses, we used Indo – WordNet lexical database as the source of senses.  Here we adopt a current method of measuring semantic relatedness between the concepts of the Kannada words taken from Indo – WordNet. This measure is dependent on identifying and counting the number of common words present between the glosses of a pair of concepts in accordance with Indo – WordNet.

  • References

    1. [1] M.Lesk. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In proceedings of SIGDOC ’86, 1986.

      [2] HindiWordNet,http://www.cfilt.iitb.ac.in/wordnet/webhwn/wn.php

      [3] Budanitsky, A., Hirst, G.: Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics 32(1), 13–47 (2006)

      [4] Hirst, G., St-Onge, D.: Lexical chains as representations of context for the detection and correction of malapropisms. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 305–332 (1998)

      [5] Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 265–283 (1998)

      [6] Lin, D.: Using syntactic dependency as a local context to resolve word sense ambiguity. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pp. 64–71 (1997)

      [7] Resnik, P.: Using information content to evaluate semantic similarity in taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, pp. 448–453 (1995)

      [8] Reddy, S., Inumella, A., Singh, N., Sangal, R.: Hindi Semantic Category Labeling using Semantic Relatedness Measures. In: Proceedings of Global WordNet Conference (2010)

      [9] Sinha, R., Mihalcea, R.: Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity. In: Proceedings of the International Conference on Semantic Computing, ICSC 2007, pp. 363–369 (2007)

      [10] Torres, S., Gelbukh, A.: Comparing Similarity Measures for Original WSD Lesk Algorithm Advances in Computer Science and Applications. Research in Computing Science 43, 155–166 (2009)

      [11] Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings on International Conference on Research in Computational Linguistics, Taiwan, (1997)

      [12] Khapra, M., Bhattacharyya, P., Chauhan, S., Nair, S., Sharma, A.: Domain Specific Iterative Word Sense Disambiguation in a Multilingual Set-ting. In: Proceedings of International Conference on NLP (ICON 2008), Pune, India (2008)

      [13] Singh, S., Siddiqui, T.J.: Evaluating Effect of Context Window Size, Stemming and Stop Word Removal on Hindi Word Sense Disambiguation. In: Proceedings of the International Conference on Information Retrieval & Knowledge Management, CAMP 2012, Malaysia, pp. 1–5 (2012)

      [14] Sinha, M., Kumar, M., Pande, P., Kashyap, L., Bhattacharyya, P.: Hindi Word Sense Disambiguation. In: International Symposium on Machine Translation, Natural Language Processing and Translation Support Systems, Delhi, India (2004)

      [15] Banerjee S, Pedersen T. An adapted Lesk algorithm for word sense disambiguation using WordNet. InInternational Conference on Intelligent Text Processing and Computational Linguistics 2002 Feb 17 (pp. 136-145). Springer, Berlin, Heidelberg.

      [16] http://www.tdil-dc.in/index.php?option=com_download&task=showDetails&toolid=1884&lang=en

      [17] B.H.ManjunathaKumar,Dr.M.Siddappa,Dr.J.Prakash "Unsupervised Kannada Stemmer using Partial Lemmatizer and Indo – Word Net" Vol. 7 - Issue 12 (December - 2017), International Journal of Engineering Research and Applications (IJERA) , ISSN: 2248-9622 , www.ijera.com

      [18] R. Sawhney and A. Kaur, "A modified technique for Word Sense Disambiguation using Lesk algorithm in Hindi language," 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), New Delhi, 2014, pp. 2745-2749.
      doi: 10.

      [19] B. H. Manjunatha Kumar, “A Survey on Word Sense Disambiguationâ€, Language in India www.languageinindia.com ISSN 1930-2940 18:2 February 2018

      [20] A. R. Pal, D. Saha and S. K. Naskar, "Word sense disambiguation in Bengali: A knowledge based approach using Bengali WordNet," 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, 2017, pp. 1-5. doi: 10.1109/ICECCT.2017.8117900

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  • How to Cite

    Manjunatha Kumar, B., M.Siddappa, D., & J.Prakash, D. (2018). Kannada word sense disambiguation by finding the overlaps between the concepts. International Journal of Engineering & Technology, 7(2.6), 189-192. https://doi.org/10.14419/ijet.v7i2.6.10565

    Received date: 2018-03-24

    Accepted date: 2018-03-24

    Published date: 2018-03-11