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Sense Disambiguation Technique for Information Retrieval in Web Search

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Advances in Computing and Information Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 178))

Abstract

Word Sense Disambiguation is the process of removing and resolving the ambiguity between words. One of the major applications of Word Sense Disambiguation (WSD) is Information Retrieval (IR). In Information Retrieval WSD helps in improving term indexing, if the senses are included as index terms. The order, in which the documents appear as the result of some search on the web, should not be based on their page ranks alone. Some other factors should also be considered while ranking the pages. This paper focuses on the technique that will describe how senses of words can play an important role in ranking the pages, especially when the word is polysemous. While adopting this technique user can receive only relevant pages on the top of the search result.

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References

  1. Veronis, J.: Sense Tagging: Don’t Look for the Meaning But for the Use. In: Workshop on Computational Lexicography and Multimedia Dictionaries, Patras, Greece, pp. 1–9 (2000)

    Google Scholar 

  2. Lesk, M.: Automatic Sense Disambiguation: How to Tell a Pine Cone from and Ice Cream Cone. In: Proceedings of the SIGDOC 1986 Conference. ACM (1986)

    Google Scholar 

  3. Galley, M., McKeown, K.: Improving Word Sense Disambiguation in Lexical Chaining. In: International Joint Conferences on Artificial Intelligence (2003)

    Google Scholar 

  4. Agirre, E., et al.: Combining supervised and unsupervised lexical knowledge methods for word sense disambiguation. Computer and the Humanities 34, 103–108 (2000)

    Article  Google Scholar 

  5. Mihalcea, R., Moldovan, D.: An Iterative Approach to Word Sense Disambiguation. In: Proceedings of Flairs, Orlando, FL, pp. 219–223 (2000)

    Google Scholar 

  6. Kwong, O.Y.: Word Sense Selection in Texts: An Integrated Model. Doctoral Dissertation, University of Cambridge (2000)

    Google Scholar 

  7. Yarowsky, D.: Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In: Meeting of the Association for Computational Linguistics, pp. 189–196 (1995)

    Google Scholar 

  8. Yarowsky, D.: Word Sense Disambiguation Using Statistical Models of Roget’s Categories Trained on Large Corpora. In: Proceedings of COLING 1992, Nantes, France, pp. 454–460 (July 1992)

    Google Scholar 

  9. Chodorow, M., Leacock, C., Miller, G.: A Topical/Local Classifier for Word Sense Identification. Computers and the Humanities 34(2000), 115–120 (2000)

    Article  Google Scholar 

  10. Bruce, R., Wiebe, J.: Decomposable modeling in natural language processing. Computational Linguistics 25(2) (1999)

    Google Scholar 

  11. O’Hara, T., Wiebe, J., Bruce, R.: Selecting Decomposable Models for Word Sense disambiguation: The Grling-Sdm System. Computers and the Humanities 34, 159–164 (2000)

    Article  Google Scholar 

  12. Daelemans, W., et al.: TiMBL: Tilburg Memory Based Learner V2.0 Reference Guide, ILK Technical Report- ILK 99-01 (1999)

    Google Scholar 

  13. Fellbaum, C., Palmer, M.: Manual and Automatic Semantic Annotation with WordNet. In: Proceedings of NAACL Workshop (2001)

    Google Scholar 

  14. Berger, A., et al.: A maximum entropy approach to natural language processing. Computational Linguistics 22(1) (1996)

    Google Scholar 

  15. Dempster, A., et al.: Maximum Likelihood from Incomplete Data via the EM Algorithm. J. Royal Statist Soc. Series B 39, 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  16. Zhou, X., Han, H.: Survey of Word Sense Disambiguation Approaches. In: 18th FLAIRS Conference, Clearwater Beach, Florida (2005)

    Google Scholar 

  17. Hastings, P., et al.: Inferring the meaning of verbs from context. In: Proceedings of the Twentieth Annual Conference of the Cognitive Science Society (CogSci 1998), Wisconsin, Madison (1998)

    Google Scholar 

  18. Bhattacharya, I., Getoor, L., Bengio, Y.: Unsupervised sense disambiguation using bilingual probabilistic models. In: Proceedings of the Annual Meeting of ACL (2004)

    Google Scholar 

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Correspondence to Rekha Jain .

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Jain, R., Purohit, G.N. (2013). Sense Disambiguation Technique for Information Retrieval in Web Search. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31600-5_44

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  • DOI: https://doi.org/10.1007/978-3-642-31600-5_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31599-2

  • Online ISBN: 978-3-642-31600-5

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