Learning Lexicon Models from Search Logs for Query Expansion

Jianfeng Gao, Shasha Xie, Xiaodong He, and Alnur Ali

Abstract

This paper explores log-based query expansion (QE) models for Web search. Three lexicon models are proposed to bridge the lexical gap between Web documents and user queries. These models are trained on pairs of user queries and titles of clicked documents. Evaluations on a real world data set show that the lexicon models, integrated into a ranker-based QE system, not only significantly improve the document retrieval performance but also outperform two state-of-the-art log-based QE methods.

Details

Publication typeArticle
Published inProceedings of EMNLP
PublisherACM
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