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A Novel Genetic Algorithm for the Word Sense Disambiguation Problem

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Advances in Artificial Intelligence (Canadian AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9673))

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Abstract

Word sense disambiguation (WSD) is a task in natural language processing, which asks to identify the appropriate sense of a word according to a particular context. Several approaches were investigated to tackle the WSD problem, including genetic algorithms. In this paper, we propose a new genetic algorithm, called GAWSD, that benefits from part-of-speech tagging, domain knowledge, and gloss enrichment to find a sense to a target word. The performance of the algorithm was evaluated on fine-grained and coarse-grained standard corpora. The results show that GAWSD outperformed the best known algorithms on the fine-grained corpus. This result sets GAWSD as a competitive algorithm for WSD.

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Notes

  1. 1.

    http://nlp.stanford.edu/software/tagger.shtml.

  2. 2.

    http://www.ranks.nl/stopwords.

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Authors and Affiliations

  1. Department of Computer Science, CCIS, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia

    Wojdan Alsaeedan & Mohamed El Bachir Menai

Authors
  1. Wojdan Alsaeedan
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  2. Mohamed El Bachir Menai
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Corresponding author

Correspondence to Mohamed El Bachir Menai .

Editor information

Editors and Affiliations

  1. Lakehead University, Thunder Bay, Ontario, Canada

    Richard Khoury

  2. National Research Council Canada , Ottawa, Canada

    Christopher Drummond

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Alsaeedan, W., Menai, M.E.B. (2016). A Novel Genetic Algorithm for the Word Sense Disambiguation Problem. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-34111-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34110-1

  • Online ISBN: 978-3-319-34111-8

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