A 2-poisson model for probabilistic coreference of named entities for improved text retrieval

  • Authors:

  • Seung-Hoon Na;Hwee Tou Ng

  • Affiliations:

  • National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore

  • Venue:
  • Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2009

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Abstract

Text retrieval queries frequently contain named entities. The standard approach of term frequency weighting does not work well when estimating the term frequency of a named entity, since anaphoric expressions (like he, she, the movie, etc) are frequently used to refer to named entities in a document, and the use of anaphoric expressions causes the term frequency of named entities to be underestimated. In this paper, we propose a novel 2-Poisson model to estimate the frequency of anaphoric expressions of a named entity, without explicitly resolving the anaphoric expressions. Our key assumption is that the frequency of anaphoric expressions is distributed over named entities in a document according to the probabilities of whether the document is elite for the named entities. This assumption leads us to formulate our proposed Co-referentially Enhanced Entity Frequency (CEEF). Experimental results on the text collection of TREC Blog Track show that CEEF achieves significant and consistent improvements over state-of-the-art retrieval methods using standard term frequency estimation. In particular, we achieve a 3% increase of MAP over the best performing run of TREC 2008 Blog Track.