Anaphora resolution of Japanese zero pronouns with deictic reference

Abstract

This paper proposes a method to resolve the reference of deictic Japanese zero pronouns which can be implemented in a practical machine translation system. This method focuses on semantic and pragmatic constraints such as semantic constraints on cases, modal expressions, verbal semantic attributes and conjunctions to determine the deictic reference of Japanese zero pronouns. This method is highly effective because the volume of knowledge that must be prepared beforehand is not very large and its precision of resolution is good. This method was implemented in the Japanese-to-English machine translation system, ALT-J/E. According to a window test for 175 zero pronouns with deictic referent in a sentence set for the evaluation of Japaneseto-English machine translation systems, all of zero pronouns could be resolved consistently and correctly.

Zero-anaphora resolution by learning rich syntactic pattern features

“…In AR, the research trend has been shifting from rule-based approaches [Baldwin 1995;Lappin and Leass 1994;Mitkov 1997] to empirical, or corpus-based, approaches [McCarthy and Lehnert 1995;Ge et al 1998;Soon et al 2001;Ng and Cardie 2002;Strube and Müller 2003;Yang et al 2003;Ng 2004;Yang et al 2005] because the latter are shown to be a cost-efficient solution achieving a performance that is comparable to best-performing rule-based systems (see the Coreference task in Message Understanding Conference (MUC) 1 and the Entity Detection and Tracking task in the Automatic Content Extraction (ACE) program 2 ). The same trend is observed also in Japanese zero-anaphora resolution, where the findings made in rule-based or theory-oriented work [Kameyama 1986;Walker et al 1994;Nakaiwa and Shirai 1996;Okumura and Tamura 1996;Murata and Nagao 1997] have been successfully incorporated in machine-learning-based frameworks [Seki et al 2002;Iida et al 2003;Isozaki and Hirao 2003]. …”

Intra-sentential Zero Anaphora Resolution using Subject Sharing Recognition

“…Traditional approaches to zero anaphora resolution are based on manually created heuristic rules (Kameyama, 1986;Walker et al, 1994;Okumura and Tamura, 1996;Nakaiwa and Shirai, 1996), which are mainly motivated by the rules and preferences introduced in Centering Theory (Grosz et al, 1995). However, the research trend of zero anaphora resolution has shifted from such rule-based approaches to machine learningbased approaches because in machine learning we can easily integrate many different types of information, such as morpho-syntactic, semantic and discourse-related information.…”

Untitled

“…There have been several efforts to computationally deal with this challenge. See LeNagard & Koehn (2010) and Nakaiwa & Shirai (1996). Identification is not the problem, when they read.…”