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We predict entity type distributions in Web search queries via probabilistic inference in graphical models that capture how entitybearing queries are generated. We jointly model the interplay between latent user intents that govern queries and unobserved entity 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. | Mining Entity Types from Query Logs via User Intent Modeling Patrick Pantel Microsoft Research One Microsoft Way Redmond WA 98052 USA ppantel@microsoft.com Thomas Lin Computer Science Engineering University of Washington Seattle Wa 98195 UsA tlin@cs.washington.edu Michael Gamon Microsoft Research One Microsoft Way Redmond WA 98052 USA mgamon@microsoft.com Abstract We predict entity type distributions in Web search queries via probabilistic inference in graphical models that capture how entitybearing queries are generated. We jointly model the interplay between latent user intents that govern queries and unobserved entity 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 models are efficiently trained using maximum likelihood 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. 1 Introduction Commercial search engines are providing ever-richer experiences around entities. Querying for a dish on Google yields recipe filters such as cook time calories and ingredients. Querying for a movie on Yahoo triggers user ratings cast tweets and showtimes. Bing further allows the movie to be directly added to the user s Netflix queue. Entity repositories such as Freebase IMDB Facebook Pages Factual Pricegrabber and Wikipedia are increasingly leveraged to enable such experiences. There are however inherent problems in the entity repositories a coverage although coverage of head entity types is often reliable the tail can be sparse b noise created by spammers extraction 563 errors or errors in crowdsourced content c ambiguity multiple types or entity identifiers are often associated with the same surface string and d .
