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Using Web Search Logs to Identify Query Classification Terms

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3 Author(s)
Taksa, I. ; Baruch Coll., City Univ. of New York, NY ; Zelikovitz, S. ; Spink, A.

Classification of search queries is a complex and computationally challenging task. Typically, search queries are short, reveal very few features per single query and are therefore a weak source for traditional machine learning. In this paper, we present a method that combines limited manual labeling, computational linguistics and information retrieval to classify a large collection of Web search queries. A short set of manually chosen terms that are known a priori to be of interest to a particular class is used to cull a small number of actual queries from a commercial search engine log. These queries are then submitted to a commercial search engine and the returned search results are used to find more class related terms. We examine classification proficiency of the proposed method on a large Web search engine query log and show that up to 48% of the unlabeled set could be classified using this method. We discuss results of this research and its implications on the advancement of short text classification

Published in:

Information Technology, 2007. ITNG '07. Fourth International Conference on

Date of Conference:

2-4 April 2007

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