A feature-free search query classification approach using semantic distance

Full text for this resource is not available from the Research Repository.

Li, Lin, Zhong, Luo, Xu, Guandong and Kitsuregawa, Masaru (2012) A feature-free search query classification approach using semantic distance. Expert Systems with Applications, 39 (12). pp. 10739-10748. ISSN 0957-4174


When classifying search queries into a set of target categories, machine learning based conventional approaches usually make use of external sources of information to obtain additional features for search queries and training data for target categories. Unfortunately, these approaches rely on large amount of training data for high classification precision. Moreover, they are known to suffer from inability to adapt to different target categories which may be caused by the dynamic changes observed in both Web topic taxonomy and Web content. In this paper, we propose a feature-free classification approach using semantic distance. We analyze queries and categories themselves and utilizes the number of Web pages containing both a query and a category as a semantic distance to determine their similarity. The most attractive feature of our approach is that it only utilizes the Web page counts estimated by a search engine to provide the search query classification with respectable accuracy. In addition, it can be easily adaptive to the changes in the target categories, since machine learning based approaches require extensive updating process, e.g., re-labeling outdated training data, re-training classifiers, to name a few, which is time consuming and high-cost. We conduct experimental study on the effectiveness of our approach using a set of rank measures and show that our approach performs competitively to some popular state-of-the-art solutions which, however, frequently use external sources and are inherently insufficient in flexibility.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/23436
DOI 10.1016/j.eswa.2012.02.191
Official URL http://www.sciencedirect.com/science/article/pii/S...
Subjects Historical > FOR Classification > 0801 Artificial Intelligence and Image Processing
Historical > FOR Classification > 0806 Information Systems
Historical > Faculty/School/Research Centre/Department > Centre for Applied Informatics
Current > Division/Research > College of Science and Engineering
Keywords ResPubID25504, search query, semantic distance, page count, classification
Citations in Scopus 11 - View on Scopus
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login