Welcome to the InfoSci Platform
Non-Topical Classification of Query Logs Using Background Knowledge

Non-Topical Classification of Query Logs Using Background Knowledge

Isak Taksa, Sarah Zelikovitz, Amanda Spink
Copyright: © 2012 |Pages: 18
ISBN13: 9781609608187|ISBN10: 1609608186|EISBN13: 9781609608194
DOI: 10.4018/978-1-60960-818-7.ch314
Cite Chapter Cite Chapter

MLA

Taksa, Isak, et al. "Non-Topical Classification of Query Logs Using Background Knowledge." Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 598-615. https://doi.org/10.4018/978-1-60960-818-7.ch314

APA

Taksa, I., Zelikovitz, S., & Spink, A. (2012). Non-Topical Classification of Query Logs Using Background Knowledge. In I. Management Association (Ed.), Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 598-615). IGI Global. https://doi.org/10.4018/978-1-60960-818-7.ch314

Chicago

Taksa, Isak, Sarah Zelikovitz, and Amanda Spink. "Non-Topical Classification of Query Logs Using Background Knowledge." In Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 598-615. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-818-7.ch314

Export Reference

Mendeley
Favorite

Abstract

Background knowledge has been actively investigated as a possible means to improve performance of machine learning algorithms. Research has shown that background knowledge plays an especially critical role in three atypical text categorization tasks: short-text classification, limited labeled data, and non-topical classification. This chapter explores the use of machine learning for non-hierarchical classification of search queries, and presents an approach to background knowledge discovery by using information retrieval techniques. Two different sets of background knowledge that were obtained from the World Wide Web, one in 2006 and one in 2009, are used with the proposed approach to classify a commercial corpus of web query data by the age of the user. In the process, various classification scenarios are generated and executed, providing insight into choice, significance and range of tuning parameters, and exploring impact of the dynamic web on classification results.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.