Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/3077

Learning-based Web query understanding

Authors Shen, Dou
Issue Date 2007
Summary Web search is attracting more and more attention with the exponential increase in data becoming available on the Web. Typically, a Web user submits a short Web query consisting of a few words to a Web search system and expects the system to return the desired information. How to understand Web queries precisely and thoroughly lies at the heart of Web search. In this thesis, we study the Web query understanding problem from several viewpoints. The first one is through query topic classification, which aims at interpreting queries in terms of predefined categories based on the queries' topics. We propose three solutions for different scenarios in query topic classification. The second way of understanding queries is to detect the type of a Web query, such as whether it requires locality information or information about a person, or the like. In this thesis, we focus on personal name detection in Web queries. The third way is to construct query hierarchies from query logs. These hierarchies reflect the generalization/specification relationship among Web queries. Finally, we study the contribution of query logs to Web-page classification, which can in turn help understand Web queries. Experimental results on some real-world data sets validate the effectiveness of our proposed methods.
Note Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007
Subjects
Language English
Format Thesis
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