Learning a Robust Word Sense Disambiguation Model using Hypernyms in Definition Sentences

Learning a Robust Word Sense Disambiguation Model using Hypernyms in Definition Sentences Kiyoaki Shirai, Tsunekazu Yagi School of Information Science, Japan Advanced Institute of Science and Technology 1-1, Asahidai, Tatsunokuchi, 923-1292, Ishikawa, Japan {kshirai,t-yagi}@jaist.ac.jp Abstract This paper proposes a method to improve the robustness of a word sense disambigua- tion (WSD) system for Japanese. Two WSD classifiers are trained from a word sense-tagged corpus: one is a classifier ob- tained by supervised learning, the other is a classifier using hypernyms extracted from definition sentences in a dictionary. The for- mer will be suitable for the disambiguation of high frequency words, while the latter is appropriate for low frequency words. A ro- bust WSD system will be constructed by combining these two classifiers. In our ex- periments, the F-measure and applicability of our proposed method were 3.4% and 10% greater, respectively, compared with a single classifier obtained by supervised learning. 1 Introduction Word sense disambiguation (WSD) is the pro- cess of selecting the appropriate meaning or sense for a given word in a document. Obvi- ously, WSD is one of the fundamental and im- portant processes needed for many natural lan- guage processing (NLP) applications. Over the past decade, many studies have been made on WSD of Japanese. Most current research uses machine learning techniques (Li and Takeuchi, 1997; Murata et al., 2001; Takamura et al., 2001), has achieved good performance. How- ever, as supervised learning methods require word sense-tagged corpora, they often suffer from data sparseness, i.e., words which do not occur frequently in a training corpus can not be disambiguated. Therefore, we cannot use su- pervised learning algorithms alone in practical NLP applications, especially when it is neces- sary to disambiguate both high frequency and low frequency words. To tackle this problem, this paper proposes a method to combine two WSD classifiers. One is a classifier obtained by supervised learning. The learning algorithm used for this classifier is the Support Vector Machine (SVM); this clas- sifier will work well for the disambiguation of high frequency words. The second classifier is the Naive Bayes model, which will work well for the disambiguation of low frequency words. In this model, hypernyms extracted from defi- nition sentences in a dictionary are considered in order to overcome data sparseness. The details of the SVM classifier are de- scribed in Section 2, and the Naive Bayes model in Section 3. The combination of these two clas- sifiers is described in Section 4. The experi- mental evaluation of the proposed method is re- ported in Section 5. We mention some related works in Section 6, and conclude the paper in Section 7. 2 SVM Classifier The first classifier is the SVM classifier. Since SVM is a supervised learning algorithm, a word sense-tagged corpus is required as training data, and the classifier can not be used to disam- biguate words which do not occur frequently in the data. However, as the effectiveness of SVM has been widely reported for a variety of NLP tasks including WSD …

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