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|Title: ||Query expansion using term relationships in language models for information retrieval.|
|Authors: ||Bai, Jing|
Bruza, Peter D.
|Keywords: ||Language model|
|Issue Date: ||Nov-2005|
|Publisher: ||ACM Press|
|Citation: ||BAI, J., SONG, D., BRUZA, P. D., NIE, J. Y. and CAO, G., 2005.
Query expansion using term relationships in language models for
information retrieval. In: A. CHOWDHURY, N. FUHR, M.
RONTHALER, H.-J. SCHEK and W. TEIKEN, eds. Proceedings of the
14th ACM International Conference on Information and Knowledge
Management. 31 October – 5 November 2005. New York : ACM
Press. pp. 688-695.|
|Abstract: ||Language Modeling (LM) has been successfully applied to
Information Retrieval (IR). However, most of the existing LM
approaches only rely on term occurrences in documents, queries
and document collections. In traditional unigram based models,
terms (or words) are usually considered to be independent. In
some recent studies, dependence models have been proposed to
incorporate term relationships into LM, so that links can be
created between words in the same sentence, and term
relationships (e.g. synonymy) can be used to expand the
document model. In this study, we further extend this family of
dependence models in the following two ways: (1) Term
relationships are used to expand query model instead of document
model, so that query expansion process can be naturally
implemented; (2) We exploit more sophisticated inferential
relationships extracted with Information Flow (IF). Information
flow relationships are not simply pairwise term relationships as
those used in previous studies, but are between a set of terms and
another term. They allow for context-dependent query expansion.
Our experiments conducted on TREC collections show that we
can obtain large and significant improvements with our approach.
This study shows that LM is an appropriate framework to
implement effective query expansion.|
|Appears in Collections:||Conference publications (Computing)|
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