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Machine Learning Approach to Search Query Classification

Machine Learning Approach to Search Query Classification

Isak Taksa, Sarah Zelikovitz, Amanda Spink
Copyright: © 2012 |Pages: 16
ISBN13: 9781609608187|ISBN10: 1609608186|EISBN13: 9781609608194
DOI: 10.4018/978-1-60960-818-7.ch308
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MLA

Taksa, Isak, et al. "Machine Learning Approach to Search Query Classification." Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 467-482. https://doi.org/10.4018/978-1-60960-818-7.ch308

APA

Taksa, I., Zelikovitz, S., & Spink, A. (2012). Machine Learning Approach to Search Query Classification. In I. Management Association (Ed.), Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 467-482). IGI Global. https://doi.org/10.4018/978-1-60960-818-7.ch308

Chicago

Taksa, Isak, Sarah Zelikovitz, and Amanda Spink. "Machine Learning Approach to Search Query Classification." In Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 467-482. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-818-7.ch308

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Abstract

Search query classification is a necessary step for a number of information retrieval tasks. This chapter presents an approach to non-hierarchical classification of search queries that focuses on two specific areas of machine learning: short text classification and limited manual labeling. Typically, search queries are short, display little class specific information per single query and are therefore a weak source for traditional machine learning. To improve the effectiveness of the classification process the chapter introduces background knowledge discovery by using information retrieval techniques. The proposed approach is applied to a task of age classification of a corpus of queries from a commercial search engine. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters.

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