An Empirical Study of Classifier Combination Based Word Sense Disambiguation

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for c...

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Bibliographic Details
Published inIEICE Transactions on Information and Systems Vol. E101.D; no. 1; pp. 225 - 233
Main Authors LU, Wenpeng, WU, Hao, JIAN, Ping, HUANG, Yonggang, HUANG, Heyan
Format Journal Article
LanguageEnglish
Published Tokyo The Institute of Electronics, Information and Communication Engineers 01.01.2018
Japan Science and Technology Agency
Subjects
Ambiguity
classifier combination
Classifiers
Data mining
probability weighted voting method
self-adaptation
State-of-the-art reviews
Voting
Word sense disambiguation
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Summary:Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.
ISSN:0916-8532
1745-1361
DOI:10.1587/transinf.2017EDP7090