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 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.

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

We cast the task as performing probabilistic inference in a graphical model that captures how queries are generated, and then apply the model to contextually recognize entity types in new 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.

We theorize that search queries are governed by a latent user intent, which in turn influences the entity types , the choice of query words, and the clicked hosts.

entity type with latent user intents, and by incorporating click signals.

Local+Type This system adds entity type features to the previous system.

This allows us to compare performance of using global features against entity type features.

To determine entity types , we link named entities to Wikipedia pages using the Wikifier (Rati-nov et al., 2011) package and extract categories from the Wikipedia page.

fine-grained entity types of two arguments, to handle polysemy.

However, such fine grained entity types come at a high cost.

It is difficult to discover a high-quality set of fine-grained entity types due to unknown criteria for developing such a set.

documents 1,303,000 entity pairs 2,017,000 (matched to Freebase) 129,000 (with entity types ) 913,000 frequent patterns 3,084 relations 24

In Experiment 1, since we needed entity types for patterns, we restricted ourselves to entities matched with Freebase, which also provides entity types for entities.

Entity types are omitted in patterns.

(2011) applied a rule-based method to the problem by using popular entity types and keywords for each relation.

We define a pattern as the entity types of an entity pair2 as well as the sequence of words on the path of the dependency parse tree from the first entity to the second one.

2If we use a standard named entity tagger, the entity types are Person, Location, and Organization.

Table 3 reports the NER performance of our method for each entity type, from which we see that our system consistently yields better F1 on all entity types than S B R. We also see that our system boosts the F1 for ORGANIZATION most significantly, reflecting the fact that a large number of organizations that are incorrectly labeled as PERSON by S B R, are now correctly recognized by our method.

Table 3: F1 (%) of NER on different entity types .

To resolve NER, we assign a label to each word in a tweet, indicating both the boundary and entity type .

tiflfil, respectively, refer to the same entity if and only if: 1) The two mentions share the same entity type ; 2) is a substring of film” or vise versa; ands) 23%.. = 1, z' = .27. andj = .71.--- .32, if 277%” exists.

It consists of 57 entity types and 79 relations.

The entity types include Agent, PhysicalThing, Event, TimeLocation, Gender, and Group, each with several subtypes.

LIME learned several rules that had only entity types in their bodies.

Unlike our system and others (Carlson et al., 2010; Doppa et al., 2010; Sorower et al., 2011) that use a predefined ontology, they automatically identify a set of entity types and relations using “open IE.” They use HOLMES (Schoenmackers et al., 2008), an inference engine based on MLNs (Domingos and Lowd, 2009) (an SRL approach that combines first-order logic and Markov networks) to infer additional facts.