A high-performance coreference resolution system using a constraint-based multi-agent strategy

TLDR: This paper presents a constraint-based multi-agent strategy to coreference resolution of general noun phrases in unrestricted English text and finds that the most recent antecedent always contains little information to directly determine the coreference relationship with the anaphor.

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.