Japanese Zero Pronoun Resolution based on Ranking Rules and Machine Learning

Japanese Zero Pronoun Resolution based on Ranking Rules and Machine Learning Hideki Isozaki and Tsutomu Hirao NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation 2-4 Hikaridai, Seika-cho, Souraku-gun, Kyoto, Japan, 619-0237 (isozaki,hirao)@cslab.kecl.ntt.co.jp Abstract Anaphora resolution is one of the most important research topics in Natural Lan- guage Processing. In English, overt pro- nouns such as she and definite noun phrases such as the company are anaphors that refer to preceding entities (an- tecedents). In Japanese, anaphors are of- ten omitted, and these omissions are called zero pronouns. There are two major ap- proaches to zero pronoun resolution: the heuristic approach and the machine learn- ing approach. Since we have to take var- ious factors into consideration, it is diffi- cult to find a good combination of heuris- tic rules. Therefore, the machine learn- ing approach is attractive, but it requires a large amount of training data. In this paper, we propose a method that com- bines ranking rules and machine learning. The ranking rules are simple and effective, while machine learning can take more fac- tors into account. From the results of our experiments, this combination gives better performance than either of the two previ- ous approaches. 1 Introduction Anaphora resolution is an important research topic in Natural Language Processing. For instance, machine translation systems should identify an- tecedents of anaphors (such as he or she) in the source language to achieve better translation quality in the target language. We are now studying open-domain question an- swering systems1, and we expect QA systems to benefit from anaphora resolution. Typical QA sys- tems try to answer a user’s question by finding rel- evant phrases from large corpora. When a correct answer phrase is far from the keywords given in the question, the systems will not succeed in find- ing the answer. If the system can correctly resolve anaphors, it will find keywords or answers repre- sented by anaphors, and the chances of finding the answer will increase. From this motivation, we are developing our system toward the ability to resolve anaphors in full-text newspaper articles. In Japanese, anaphors are often omitted and these omissions are called zero pronouns. Since they do not give any hints (e.g., number or gender) about an- tecedents, automatic zero pronoun resolution is dif- ficult. In this paper, we focus on resolving the zero pronoun, which is shortened for simplicity to ‘zero.’ Most studies on Japanese zero pronoun resolution have not tried to resolve zeros in full-text newspa- per articles. They have discussed simple sentenses (Kameyama, 1986; Walker et al., 1994; Yamura- Takei et al., 2002), dialogues (Yamamoto et al., 1997), stereotypical lead sentences of newspaper ar- ticles (Nakaiwa and Ikehara, 1993), intrasentential resolution (Nakaiwa and Ikehara, 1996; Ehara and Kim, 1996) or organization names in newspaper ar- ticles (Aone and Bennett, 1995). There are two approaches to the problem: the heuristic approach and the machine learning ap- 1http://trec.nist.gov/data/qa.html proach. The Centering Theory (Grosz et al., 1995) is important in the heuristic approach. Walker et al. …

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