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.

Jointly modeling the interplay between the underlying user intents and entity types in web search queries shows significant improvements over the current state of the art on the task of resolving entity types in head queries.

We show that jointly modeling user intent and entity type significantly outperforms the current state of the art on the task of entity type resolution in queries.

0 We empirically show that our models outperform the state of the art and that modeling latent intent contributes significantly to these results.

Our ASR system is based on the 2007 IBM Speech transcription system for the GALE Distillation Go/No-go Evaluation (Chen et al., 2006) with state of the art discriminative acoustic models.

(2010), a state of the art broadcast news (BN) parser, with phrase structures converted to labeled dependencies by the Stanford converter.

Discriminative models, which directly distinguish correct from incorrect hypothesis, are particularly attractive because they allow the inclusion of arbitrary features (Kuo et al., 2002; Roark et al., 2007; Collins et al., 2005); these models with syntactic information have obtained state of the art results.