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Comprehensive Method of Knowledge-Based Approach for Word-Sense Disambiguation

  • Pornima GidheEmail author
  • Leena Ragha
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)

Abstract

We share our knowledge, thoughts, and information on the Web through our Natural Language (NL). Most of the words in NL are ambiguous and change the meaning of a sentence. Humans can disambiguate the meaning through perceived intelligence, but it is challenging task for a system. Many researchers are working on Word-Sense Disambiguation (WSD) which is used to get correct sense out of context to make the sense of a text understandable by machine/application. We focus on Knowledge-Based (KB) approaches which rely on knowledge resource like WordNet. We compared KB algorithms such as Lesk, Walker, and Conceptual Density with the help of common dataset of sentences. Comparative analysis is done to find the limitations of individual algorithms based on the analysis; we propose a comprehensive method of KB approach.

Keywords

Knowledge based Word-sense disambiguation (WSD) Natural language processing (NLP) 

References

  1. 1.
    Fard, M.H.: Word sense disambiguation based on gloss expansion. In: Information and Knowledge Technology (IKT), pp. 7–10, IEEE (2014)Google Scholar
  2. 2.
    Dhungana, U.R: Word Sense Disambiguation using WSD specific WordNet of polysemy words, Semantic Computing (ICSC), pp. 148–152, IEEE (2015)Google Scholar
  3. 3.
    Giyanani, R.: A survey on word sense disambiguation. IOSR J. Comput. Eng. (IOSR-JCE) 14, 30–33 (2013)CrossRefGoogle Scholar
  4. 4.
    Kalita, P.: Word sense disambiguation: a survey. Int. J. Eng. Comput. Sci. 4, 11743–11748 (2015)Google Scholar
  5. 5.
    Singh, H., Gupta, V.: Performance analysis of recent Word Sense disambiguation techniques. In: 2nd International Conference on Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1–6, IEEE (2015)Google Scholar
  6. 6.
    Zhou, X., Hyoil, H.: Survey of word sense disambiguation approaches. In: FLAIRS Conference, pp. 307–313 (2005)Google Scholar
  7. 7.
    Miller, G.A.: Introduction to WordNet: an on-line lexical database. Int. J. Lexicogr. 3, 235–244 (1990)CrossRefGoogle Scholar
  8. 8.
    Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: International Conference on Intelligent Text Processing and Computational Linguistics, pp. 136–145, Springer, Berlin Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Dhungana, U.R.: Word sense disambiguation using WSD specific WordNet of polysemy words. In: International Conference on Semantic Computing (ICSC), pp. 148–152, IEEE (2015)Google Scholar
  10. 10.
    Sawhney, R., Arvinder K.: A modified technique for Word Sense disambiguation using Lesk algorithm in Hindi language. In: Advances in Computing, Communications and Informatics (ICACCI), pp. 2745–2749, IEEE (2014)Google Scholar
  11. 11.
    Kalita, P., Barman, A.K.: Implementation of Walker algorithm in Word Sense disambiguation for assamese language. In: Advanced Computing and Communication (ISACC), International Symposium on, pp. 136–140, IEEE (2015)Google Scholar
  12. 12.
    Agirre, E., Rigau, G.: Word sense disambiguation using conceptual density. In: Proceedings of the 16th Conference on Computational linguistics, vol. 2, pp. 16–22 (2008)Google Scholar
  13. 13.
    Sinha, M.: Hindi word sense disambiguation. In: International Symposium on Machine Translation, Natural Language Processing and Translation Support Systems (iSTRANS), pp. 10–17, Delhi, India (2004)Google Scholar
  14. 14.
    Farazi, F.: WordNet powered faceted semantic search with automatic sense disambiguation for bioenergy domain. In: Tenth International Conference on Semantic Computing (ICSC), pp. 112–115, IEEE (2016)Google Scholar
  15. 15.
    Agirre, E., Rigau, G.: Word sense disambiguation using conceptual density. In: Proceedings of the 16th Conference on Computational linguistics, vol. 1, pp. 16–22 (2008)Google Scholar
  16. 16.
    Fulmari, A., Manoj, B.C.: A survey on supervised learning for word sense disambiguation. Int. J. Adv. Res. Comput. Commun. Eng. 2, 4667–4670 (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Computer Engineering DepartmentRAIT MumbaiMumbaiIndia

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