Abstract
Classifying web queries into predefined target categories, also known as web query classification, is important to improve search relevance and online advertising. Web queries are however typically short, ambiguous and in constant flux. Moreover, target categories often lack standard taxonomies and precise semantic descriptions. These challenges make the web query classification task a non-trivial problem. In this paper, we present two complementary approaches for the web query classification task. First is the enrichment method that uses the World Wide Web (WWW) to enrich target categories and further models the web query classification as a search problem. Our second approach, the reductionist approach, works by reducing web queries to few central tokens. We evaluate the two approaches based on few thousands human labeled local and non-local web queries. From our study, we find the two approaches to be complementary to each other as the reductionist approach exhibits high precision but low recall, whereas the enrichment method exhibits high recall but low precision.
This work was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CUHK 413210).
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Agrawal, R., Yu, X., King, I., Zajac, R. (2011). Enrichment and Reductionism: Two Approaches for Web Query Classification. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_17
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DOI: https://doi.org/10.1007/978-3-642-24965-5_17
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