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Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context

Bing, Lidong, Lam, Wai, Wong, Tak-Lam, Jameel, Shoaib (2015) Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context. ACM Transactions on Information Systems, 33 (2). Article Number 6. ISSN 1046-8188. (doi:10.1145/2699666) (KAR id:69591)

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An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data.

Item Type: Article
DOI/Identification number: 10.1145/2699666
Depositing User: Shoaib Jameel
Date Deposited: 16 Oct 2018 11:03 UTC
Last Modified: 17 Aug 2022 11:02 UTC
Resource URI: (The current URI for this page, for reference purposes)
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