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Search Query Semantics

Search engine queries are more than just a sequence of words. Discovering the structure of a query can help search engines to identify users' search intent. As a result, search engines apply techniques such as query segmentation and word sense disambiguation in order to reveal and meet users' search requirements. A technique that has recently been applied to uncover the semantics of a query is that of named entity recognition, that is the task of extracting from text instances of different categories such as person, location, or company.

In this project, we propose a framework for the detection and classification of named entities in search queries. Typically, a web search query consists of only few words and does not provide enough context nor surface clues, such as capitalisation, to accurately detect named entities. Our framework overcomes these challenges by applying two-stage approach.

The first stage involves the recognition of candidate named entities by grammatically annotating query tokens, and sets the boundaries of named entitiess using query segmentation. The second stage involves the classification of extracted candidate named entities using the vector space model.