Improving Japanese Zero Pronoun Resolution by Global Word Sense Disambiguation

Improving Japanese Zero Pronoun Resolution by Global Word Sense Disambiguation Daisuke Kawahara and Sadao Kurohashi Graduate School of Information Science and Technology, University of Tokyo {kawahara,kuro}@kc.t.u-tokyo.ac.jp Abstract This paper proposes unsupervised word sense disambiguation based on automati- cally constructed case frames and its in- corporation into our zero pronoun resolu- tion system. The word sense disambigua- tion is applied to verbs and nouns. We consider that case frames define verb senses and semantic features in a thesaurus define noun senses, respectively, and perform sense disambiguation by selecting them based on case analysis. In addition, according to the one sense per discourse heuristic, the word sense disambiguation results are cached and applied globally to the subsequent words. We integrated this global word sense disam- biguation into our zero pronoun resolution system, and conducted experiments of zero pronoun resolution on two different domain corpora. Both of the experimental results indicated the effectiveness of our approach. 1 Introduction For a long time, parsing has been a central is- sue for the area of natural language analyses. In recent years, its accuracy has improved to over 90%, and it became the fundamental tech- nology that is applied to a lot of NLP applica- tions, such as question answering, text summa- rization, and machine translation. Accordingly, anaphora resolution, which is positioned as the next step of parsing, has been studied actively (Ng and Cardie, 2002; Yang et al., 2003; Iida et al., 2003; Isozaki and Hirao, 2003; Kawa- hara and Kurohashi, 2004). Its performance, however, is not satisfactory enough to benefit the NLP applications. We investigated errors of our Japanese zero pronoun resolution system (Kawahara and Kurohashi, 2004), and found that word sense ambiguity causes a major part of errors. Our zero pronoun resolution system uti- lizes the general-purpose thesaurus Nihongo Goi Taikei (Ikehara et al., 1997) (hereafter, NTT thesaurus) to do matching of example words. In this thesaurus, one or more semantic features are given to each word, and similarity between words can be calculated by comparing closeness of their semantic features in the thesaurus tree (Appendix A). Multiple semantic features for a word, i.e. word sense ambiguity, cause in- correct matching, and furthermore deteriorate accuracy of the zero pronoun resolution sys- tem. For instance, in the thesaurus, “gobou” (burdock/priest/temple) has four semantic fea- tures: <crop>, <vegetable>, <priest> and <temple>∗. If <priest> is used for “gobou” in a cooking domain text, though “gobou” means ‘burdock’ (a genus of coarse biennial herbs) in its context, “gobou” is identified as an agent. That is, “gobou” is incorrectly analyzed as an- tecedents of the following nominative zero pro- nouns. If such word sense ambiguity can be resolved, incorrect matching decreases, and the anaphora resolution system will improve. Word sense dis- ambiguation is a basic issue of NLP. Recently, the evaluation exercises for word sense disam- biguation such as SENSEVAL-1 (Kilgarriff and Palmer, 2000) and SENSEVAL-2 (Yarowsky, 2001) have been held, but word sense disam- biguation has rarely been incorporated into deep analyses like anaphora resolution. This paper …

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