Exploration of Coreference Resolution: The ACE Entity Detection and Recognition Task

  • Ying Chen
  • Kadri Hacioglu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4188)


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.


Similarity Matrix Noun Phrase Similarity Metrics Correlation Cluster Partial Entity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ying Chen
    • 1
  • Kadri Hacioglu
    • 1
  1. 1.Center for Spoken Language ResearchUniversity of Colorado at BoulderUSA

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