NDLI: Query expansion using term relationships in language models for information retrieval

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Query expansion using term relationships in language models for information retrieval

Content Provider	ACM Digital Library
Author	Bai, Jing Bruza, Peter Song, Dawei Nie, Jian-Yun Cao, Guihong
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.
Starting Page	688
Ending Page	695
Page Count	8
File Format	PDF
ISBN	1595931406
DOI	10.1145/1099554.1099725
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Term relationships Language model Information flow Query expansion
Content Type	Text
Resource Type	Article

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