Exploration of coreference resolution: the ACE entity detection and recognition task

Authors:
Ying Chen;Kadri Hacioglu
Affiliations:
Center for Spoken Language Research, University of Colorado at Boulder;Center for Spoken Language Research, University of Colorado at Boulder
Venue:
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Year:
2006

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Abstract

In this paper, we consider the coreference resolution problem in the context of information extraction as envisioned by the DARPA Automatic Content Extraction (ACE) program Given a set of entity mentions referring to real world entities and a similarity matrix that characterizes how similar those mentions are, we seek a set of entities that are uniquely co-referred to by those entity mentions The quality of the clustering of entity mentions into unique entities significantly depends on the quality of (1) the similarity matrix and (2) the clustering algorithm We explore the coreference resolution problem along those two dimensions and clearly show the tradeoff among several ways of learning similarity matrix and using it while performing clustering.