Queries asked on web search engines often target structured data, such as commercial products, movie showtimes, or airline schedules. However, surfacing relevant results from such data is a highly challenging problem, due to the unstructured language of the web queries, and the imposing scalability and speed requirements of web search. In this paper, we discover latent structured semantics in web queries and produce Structured Annotations for them. We consider an annotation as a mapping of a query to a table of structured data and attributes of this table. Given a collection of structured tables, we present a fast and scalable tagging mechanism for obtaining all possible annotations of a query over these tables. However, we observe that for a given query only few are sensible for the user needs. We thus propose a principled probabilistic scoring mechanism, using a generative model, for assessing the likelihood of a structured annotation, and we define a dynamic threshold for filtering out misinterpreted query annotations. Our techniques are completely unsupervised, obviating the need for costly manual labeling effort. We evaluated our techniques using real world queries and data and present promising experimental results.
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