Exploiting Syntactic Patterns as Clues in Zero-Anaphora Resolution

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 625–632, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Exploiting Syntactic Patterns as Clues in Zero-Anaphora Resolution Ryu Iida, Kentaro Inui and Yuji Matsumoto Graduate School of Information Science, Nara Institute of Science and Technology 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan {ryu-i,inui,matsu}@is.naist.jp Abstract We approach the zero-anaphora resolu- tion problem by decomposing it into intra-sentential and inter-sentential zero- anaphora resolution. For the former prob- lem, syntactic patterns of the appearance of zero-pronouns and their antecedents are useful clues. Taking Japanese as a target language, we empirically demonstrate that incorporating rich syntactic pattern fea- tures in a state-of-the-art learning-based anaphora resolution model dramatically improves the accuracy of intra-sentential zero-anaphora, which consequently im- proves the overall performance of zero- anaphora resolution. 1 Introduction Zero-anaphora is a gap in a sentence that has an anaphoric function similar to a pro-form (e.g. pro- noun) and is often described as “referring back” to an expression that supplies the information nec- essary for interpreting the sentence. For example, in the sentence “There are two roads to eternity, a straight and narrow, and a broad and crooked,” the gaps in “a straight and narrow (gap)” and “a broad and crooked (gap)” have a zero-anaphoric relationship to “two roads to eternity.” The task of identifying zero-anaphoric relations in a given discourse, zero-anaphora resolution, is essential in a wide range of NLP applications. This is the case particularly in such a language as Japanese, where even obligatory arguments of a predicate are often omitted when they are inferable from the context. In fact, in our Japanese newspa- per corpus, for example, 45.5% of the nominative arguments of verbs are omitted. Since such gaps can not be interpreted only by shallow syntac- tic parsing, a model specialized for zero-anaphora resolution needs to be devised on the top of shal- low syntactic and semantic processing. Recent work on zero-anaphora resolution can be located in two different research contexts. First, zero-anaphora resolution is studied in the con- text of anaphora resolution (AR), in which zero- anaphora is regarded as a subclass of anaphora. In AR, the research trend has been shifting from rule- based approaches (Baldwin, 1995; Lappin and Le- ass, 1994; Mitkov, 1997, etc.) to empirical, or corpus-based, approaches (McCarthy and Lehnert, 1995; Ng and Cardie, 2002a; Soon et al., 2001; Strube and M¨uller, 2003; Yang et al., 2003) be- cause the latter are shown to be a cost-efficient solution achieving a performance that is compa- rable to best performing rule-based systems (see the Coreference task in MUC1 and the Entity De- tection and Tracking task in the ACE program2). The same trend is observed also in Japanese zero- anaphora resolution, where the findings made in rule-based or theory-oriented work (Kameyama, 1986; Nakaiwa and Shirai, 1996; Okumura and Tamura, 1996, etc.) have been successfully incorporated in machine learning-based frame- works (Seki et al., 2002; Iida et al., 2003). Second, the task of zero-anaphora resolution …

Content truncated to 500 words