Please use this identifier to cite or link to this item:
|Title:||A 2-poisson model for probabilistic coreference of named entities for improved text retrieval|
|Authors:||Na, S.-H. |
|Citation:||Na, 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|
|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. Copyright 2009 ACM.|
|Source Title:||Proceedings - 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 18, 2019
WEB OF SCIENCETM
checked on Feb 11, 2019
checked on Feb 2, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.