TAILIEUCHUNG - Báo cáo khoa học: "Understanding the Semantic Structure of Noun Phrase Queries"

Determining the semantic intent of web queries not only involves identifying their semantic class, which is a primary focus of previous works, but also understanding their semantic structure. In this work, we formally define the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers. We present methods that automatically identify these constituents as well as their semantic roles based on Markov and semi-Markov conditional random fields. | Understanding the Semantic Structure of Noun Phrase Queries Xiao Li Microsoft Research One Microsoft Way Redmond WA 98052 USA xiaol@ Abstract Determining the semantic intent of web queries not only involves identifying their semantic class which is a primary focus of previous works but also understanding their semantic structure. In this work we formally define the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers. We present methods that automatically identify these constituents as well as their semantic roles based on Markov and semi-Markov conditional random fields. We show that the use of semantic features and syntactic features significantly contribute to improving the understanding performance. 1 Introduction Web queries can be considered as implicit questions or commands in that they are performed either to find information on the web or to initiate interaction with web services. Web users however rarely express their intent in full language. For example to find out what are the movies of 2010 in which johnny depp stars a user may simply query johnny depp movies 2010 . Today s search engines generally speaking are based on matching such keywords against web documents and ranking relevant results using sophisticated features and algorithms. As search engine technologies evolve it is increasingly believed that search will be shifting away from ten blue links toward understanding intent and serving objects. This trend has been largely driven by an increasing amount of structured and semi-structured data made available to search engines such as relational databases and semantically annotated web documents. Searching over such data sources in many cases can offer more relevant and essential results compared with merely returning web pages that contain query keywords. Table 1 shows a simplified view of a structured data source where each row represents a movie object. Consider the query johnny depp movies 2010

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