ABSTRACT
Web query classification (QC) aims to classify Web users' queries, which are often short and ambiguous, into a set of target categories. QC has many applications including page ranking in Web search, targeted advertisement in response to queries, and personalization. In this paper, we present a novel approach for QC that outperforms the winning solution of the ACM KDDCUP 2005 competition, whose objective is to classify 800,000 real user queries. In our approach, we first build a bridging classifier on an intermediate taxonomy in an offline mode. This classifier is then used in an online mode to map user queries to the target categories via the above intermediate taxonomy. A major innovation is that by leveraging the similarity distribution over the intermediate taxonomy, we do not need to retrain a new classifier for each new set of target categories, and therefore the bridging classifier needs to be trained only once. In addition, we introduce category selection as a new method for narrowing down the scope of the intermediate taxonomy based on which we classify the queries. Category selection can improve both efficiency and effectiveness of the online classification. By combining our algorithm with the winning solution of KDDCUP 2005, we made an improvement by 9.7% and 3.8% in terms of precision and F1 respectively compared with the best results of KDDCUP 2005.
- D. Beeferman and A. Berger. Agglomerative clustering of a search engine query log. In KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 407--416, 2000. Google ScholarDigital Library
- S. M. Beitzel, E. C. Jensen, O. Frieder, D. Grossman, D. D. Lewis, A. Chowdhury, and A. Kolcz. Automatic web query classification using labeled and unlabeled training data. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 581--582, 2005. Google ScholarDigital Library
- L. Gravano, V. Hatzivassiloglou, and R. Lichtenstein. Categorizing web queries according to geographical locality. In CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management, pages 325--333, 2003. Google ScholarDigital Library
- I.-H. Kang and G. Kim. Query type classification for web document retrieval. In SIGIR '03: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pages 64--71, 2003. Google ScholarDigital Library
- Z. T. Kardkovács, D. Tikk, and Z. Bánsághi. The ferrety algorithm for the kdd cup 2005 problem. SIGKDD Explor. Newsl., 7(2):111--116, 2005. Google ScholarDigital Library
- Y. Li, Z. Zheng, and H. K. Dai. Kdd cup-2005 report: facing a great challenge. SIGKDD Explor. Newsl., 7(2):91--99, 2005. Google ScholarDigital Library
- A. McCallum and K. Nigam. A comparison of event models for naive bayes text classication. In AAAI-98 Workshop on Learning for Text Categorization, 1998.Google Scholar
- G. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. Miller. Introduction to wordnet: an on-line lexical database. International Journal of Lexicography, 3(4):23--244, 1990.Google ScholarCross Ref
- F. Peng, D. Schuurmans, and S. Wang. Augmenting naive bayes classifiers with statistical language models. Inf. Retr., 7(3-4):317--345, 2004. Google ScholarDigital Library
- J. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers. MIT Press, 1999.Google Scholar
- D. Shen, R. Pan, J.-T. Sun, J. J. Pan, K. Wu, J. Yin, and Q. Yang. Q2c@ust: our winning solution to query classification in kddcup 2005. SIGKDD Explor. Newsl., 7(2):100--110, 2005. Google ScholarDigital Library
- R. C. van. Information Retrieval. Butterworths, London, second edition edition, 1979.Google Scholar
- D. Vogel, S. Bickel, P. Haider, R. Schimpfky, P. Siemen, S. Bridges, and T. Scheffer. Classifying search engine queries using the web as background knowledge. SIGKDD Explor. Newsl., 7(2):117--122, 2005. Google ScholarDigital Library
- J.-R. Wen, J.-Y. Nie, and H.-J. Zhang. Query clustering using content words and user feedback. In SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, pages 442--443, 2001. Google ScholarDigital Library
- Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In ICML '97: Proceedings of the Fourteenth International Conference on Machine Learning, pages 412--420, 1997. Google ScholarDigital Library
Index Terms
- Building bridges for web query classification
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