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Query enrichment for web-query classification

Dou Shen, Rong Pan, Jian-Tao Sun, Jeffrey Junfeng Pan, Kangheng Wu, Jie Yin, and Qiang Yang

Abstract

Web search queries are typically short and ambiguous. To classify these queries into certain target categories is a di±cult but important problem. In this paper, we present a new technique called query enrichment, which takes a short query and maps it to the intermediate objects. Based on the collected intermediate objects, the query is then mapped to the target categories. To build the necessary mapping functions, we use an ensemble of search engines to produce an enrichment of the queries. Our technique was applied to ACM Knowledge-discovery and data mining competition (ACM KDDCUP) in 2005, where we won the championship on all three evaluation metrics (precision, F1 measure, which combines precision and recall together, and creativity, which is judged by the organizers) among a total of 33 teams worldwide. In this paper, we show that, despite the difficulty in an abundance of ambiguous queries and a lack of training data, our query enrichment technique can solve the problem satisfactorily through a two-phase classification framework. We present a detailed description of our algorithm and experimental evaluation. Our best result of F1 and precision are 42.4% and 44.4%, respectively, which are 9.6% and 24.3% higher than those from the runner-ups, respectively.

Details

Publication typeArticle
Published inACM Transactions on Information Systems (TOIS)
URLhttp://doi.acm.org/10.1145/1165774.1165776
Pages320–352
Volume24
Number3
AddressNew York, NY, USA
PublisherACM
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