Paper: Mining Entity Types from Query Logs via User Intent Modeling

ACL ID P12-1059
Title Mining Entity Types from Query Logs via User Intent Modeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

We predict entity type distributions in Web search queries via probabilistic inference in graphical models that capture how entity- bearing queries are generated. We jointly model the interplay between latent user in- tents that govern queries and unobserved en- tity types, leveraging observed signals from query formulations and document clicks. We apply the models to resolve entity types in new queries and to assign prior type distributions over an existing knowledge base. Our mod- els are efficiently trained using maximum like- lihood estimation over millions of real-world Web search queries. We show that modeling user intent significantly improves entity type resolution for head queries over the state of the art, on several metrics, without degradation in tail query performance.