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Vol.24 No.1
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An Experimental Study on an Effective Word Sense Disambiguation Model Based on Automatic Sense Tagging Using Dictionary Information

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2007, v.24 no.1, pp.321-342
https://doi.org/10.3743/KOSIM.2007.24.1.321


& (2007). An Experimental Study on an Effective Word Sense Disambiguation Model Based on Automatic Sense Tagging Using Dictionary Information. Journal of the Korean Society for Information Management, 24(1), 321-342, https://doi.org/10.3743/KOSIM.2007.24.1.321
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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%.

keywords
단어 중의성 해소, 자동 태깅, 의미 분류, 사전 추출 정보 기반 태깅, 연어 공기 기반 태깅, word sense disambiguation, automatic tagging, sense classification, dictionary information-based tagging, collocation co-occurrence-based tagging

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