Paper: Learning Lexicon Models from Search Logs for Query Expansion

ACL ID D12-1061
Title Learning Lexicon Models from Search Logs for Query Expansion
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

This paper explores log-based query expan- sion (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 retriev- al performance but also outperform two state-of-the-art log-based QE methods.