A High-Performance Coreference Resolution System using a Constraint-based Multi-Agent Strategy

A High-Performance Coreference Resolution System using a Constraint-based Multi-Agent Strategy ZHOU GuoDong SU Jian Institute for Infocomm Research 21 Heng Mui Keng Terrace Singapore 119613 Email: zhougd@i2r.a-star.edu.sg Abstract This paper presents a constraint-based multi- agent strategy to coreference resolution of general noun phrases in unrestricted English text. For a given anaphor and all the preceding referring expressions as the antecedent candidates, a common constraint agent is first presented to filter out invalid antecedent candidates using various kinds of general knowledge. Then, according to the type of the anaphor, a special constraint agent is proposed to filter out more invalid antecedent candidates using constraints which are derived from various kinds of special knowledge. Finally, a simple preference agent is used to choose an antecedent for the anaphor form the remaining antecedent candidates, based on the proximity principle. One interesting observation is that the most recent antecedent of an anaphor in the coreferential chain is sometimes indirectly linked to the anaphor via some other antecedents in the chain. In this case, we find that the most recent antecedent always contains little information to directly determine the coreference relationship with the anaphor. Therefore, for a given anaphor, the corresponding special constraint agent can always safely filter out these less informative antecedent candidates. In this way, rather than finding the most recent antecedent for an anaphor, our system tries to find the most direct and informative antecedent. Evaluation shows that our system achieves Precision / Recall / F-measures of 84.7% / 65.8% / 73.9 and 82.8% / 55.7% / 66.5 on MUC- 6 and MUC-7 English coreference tasks respectively. This means that our system achieves significantly better precision rates by about 8 percent over the best-reported systems while keeping recall rates. 1 Introduction Coreference accounts for cohesion in texts. Especially, a coreference denotes an identity of reference and holds between two expressions, which can be named entities, definite noun phrases, pronouns and so on. Coreference resolution is the process of determining whether two referring expressions refer to the same entity in the world. The ability to link referring expressions both within and across the sentence is critical to discourse and language understanding in general. For example, coreference resolution is a key task in natural language interfaces, machine translation, text summarization, information extraction and question answering. In particular, information extraction systems like those built in the DARPA Message Understanding Conferences (MUC) have revealed that coreference resolution is such a crucial component of an information extraction system that a separate coreference task has been defined and evaluated in MUC-6 (1995) and MUC-7 (1998). There is a long tradition of work on coreference resolution within computational linguistics. Many of the earlier works in coreference resolution heavily exploited domain and linguistic knowledge (Carter 1987; Rich and LuperFoy 1988; Carbonell and Brown 1988). However, the pressing need for the development of robust and inexpensive solutions encouraged the drive toward knowledge-poor strategies (Dagan and Itai 1990; Lappin and Leass 1994; Mitkov 1998; Soon, Ng and Lim 2001; Ng and Cardie …

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