Please use this identifier to cite or link to this item: https://doi.org/10.1145/1571941.1571990
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dc.titleA 2-poisson model for probabilistic coreference of named entities for improved text retrieval
dc.contributor.authorNa, S.-H.
dc.contributor.authorNg, H.T.
dc.date.accessioned2013-07-04T08:39:41Z
dc.date.available2013-07-04T08:39:41Z
dc.date.issued2009
dc.identifier.citationNa, S.-H., Ng, H.T. (2009). A 2-poisson model for probabilistic coreference of named entities for improved text retrieval. Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009 : 275-282. ScholarBank@NUS Repository. https://doi.org/10.1145/1571941.1571990
dc.identifier.isbn9781605584836
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41949
dc.description.abstractText 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. Copyright 2009 ACM.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1145/1571941.1571990
dc.sourceScopus
dc.subjectCo-reference resolution
dc.subjectEntity retrieval
dc.subjectTerm frequency
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1145/1571941.1571990
dc.description.sourcetitleProceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009
dc.description.page275-282
dc.identifier.isiut000270976500036
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