Please use this identifier to cite or link to this item: https://doi.org/10.1145/2109211.2109213
Title: Towards the taxonomy-oriented categorization of yellow pages queries
Authors: Li, Z. 
Xiao, X.
Wang, M.
Wang, C.
Wang, X.
Xie, X.
Keywords: Algorithms
Experimentation
Issue Date: Mar-2012
Citation: Li, Z., Xiao, X., Wang, M., Wang, C., Wang, X., Xie, X. (2012-03). Towards the taxonomy-oriented categorization of yellow pages queries. ACM Transactions on Internet Technology 11 (4) : -. ScholarBank@NUS Repository. https://doi.org/10.1145/2109211.2109213
Abstract: Yellow pages search is a popular service that provides a means for finding businesses close to particular locations. The efficient search of yellow pages is becoming a rapidly evolving research area. The underlying data maintained in yellow pages search engines are typically labeled according to Standard Industry Classification (SIC) categories, and users can search yellow pages with categories according to their interests. Categorizing yellow pages queries into a subset of topical categories can help to improve search experience and quality. However, yellow pages queries are usually short and ambiguous. In addition, a yellow pages query taxonomy is typically organized by a hierarchy of a fairly large number of categories. These characteristics make automatic yellow pages query categorization difficult and challenging. In this article, we propose a flexible yellow pages query categorization approach. The proposed technique is built based on a TF-IDF similarity taxonomy matching scheme that is able to provide more accurate query categorization than previous keyword-based matching schemes. To further improve the categorization performance, we design several filtering schemes. Through extensive experimentation, we demonstrate encouraging results. We obtain F1 measures of about 0.5 and 0.3 for categorizing yellow pages queries into 19 coarse categories and 244 finer categories, respectively. We investigate different components in the proposed approach and also demonstrate the superiority of our approach over a hierarchical support vector machine classifier. © 2012 ACM 1533-5399/2012/03-ART16 $10.00.
Source Title: ACM Transactions on Internet Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/124981
ISSN: 15335399
DOI: 10.1145/2109211.2109213
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