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An Experimental Study on an Effective Word Sense Disambiguation Model Based on Automatic Sense Tagging Using Dictionary Informaiton

  • Journal of the Korean Society for Information Management
  • Abbr : JKOSIM
  • 2007, 24(1), pp.321~342
  • DOI : 10.3743/KOSIM.2007.24.1.321
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : February 28, 2007
  • Accepted : February 28, 2007
  • Published : March 30, 2007

Yong-Gu Lee 1,  Young-Mee Chung 2

1계명대학교
2연세대학교

Accredited

ABSTRACT

This study presents an effective word sense disambiguation model that does not require manual sense tagging process by automatically tagging the right sense using a machine-readable dictionary, and attempts to classify the senses of those words using a classifier built from the training data. The automatic tagging technique was implemnted by the dictionary information-based and the collocation co-occurrence-based methods. The dictionary information-based method that applied multiple feature selection showed the tagging accuracy of 70.06%, and the collocation co-occurrence-based method 56.33%. The sense classifier using the dictionary information-based tagging method showed the classification accuracy of 68.11%, and that using the collocation co-occurrence-based tagging method 62.09%. The combined tagging method applying data fusion technique achieved a greater performance of 76.09% resulting in the classification accuracy of 76.16%.

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