Query enrichment for web-query classification

ACM Transactions on Information Systems

Volumn 24, Issue 3, 2006, Pages 320-352

  Shen, Dou ^a   Pan, Rong ^a   Sun, Jian Tao ^b   Pan, Jeffrey Junfeng ^a   Wu, Kangheng ^a   Yin, Jie ^a   Yang, Qiang ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Ensemble learning; KDDCUP2005; Query classification; Query enrichment; Synonym based classifier

Indexed keywords

ENSEMBLE LEARNING; KDDCUP2005; QUERY CLASSIFICATION; QUERY ENRICHMENT; SYNONYM BASED CLASSIFIER;

CONFORMAL MAPPING; DATA MINING; OBJECT RECOGNITION; SEARCH ENGINES; WORLD WIDE WEB;

QUERY LANGUAGES;

EID: 33749595945     PISSN: 10468188     EISSN: 10468188     Source Type: Journal    
DOI: 10.1145/1165774.1165776     Document Type: Article

References (39)

1. ALPAYDIN, E. 2004. Introduction to Machine Learning. MIT Press, Cambridge, MA.
2. BAUER, E. AND KOHAVI, R. 1999. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. J. Mach. Learn. 36, 1-2, 105-139.
3. BEEPERMAN, D. AND BERGER, A. 2000. Agglomerative clustering of a search engine query log. In KDD '00: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, 407-416.
4. BEITZEL, S. M., JENSEN, E. C., FRIEDER, O., GROSSMAN, D., LEWIS, D. D., CHOWDHUHY, A., AND KOLCZ, A. 2005. 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. ACM Press, New York, 581-582.
5. CANN, A. J. 2003. Maths from Scratch for Biologists. John Wiley & Sons, New York, NY.
6. CARUANA, R., NICULESCU-MIZIL, A., CREW, G., AND KSIKES, A. 2004. Ensemble selection from libraries of models. In ICML '04: Proceedings of the 21st International Conference on Machine Learning. ACM Press, New York, 18.
7. CHANG, C.-H. AND Hsu, C.-C. 1998. Integrating query expansion and conceptual relevance feedback for personalized web information retrieval. In WWW7: Proceedings of the 7th International Conference on World Wide Web 7. Elsevier Science Publishers B. V., Amsterdam, 621-623.
8. CHEKUHI, C., GOLDWASSER, M., RAGHAVAN, P., AND UPFAL, E. 1997. Web search using automated classification. 6th International World Wide Web Conference (WWW6). Poster presentation.
9. CHEN, H. AND DUMAIS, S. 2000. Bringing order to the web: Automatically categorizing search results. In CHI '00: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press, New York, 145-152.
10. DIETTERICH, T. G. 2000. Ensemble methods in machine learning. In. Multiple Classifier Systems. 1-15.
11. FAN, W., STOLFO, S. J., AND ZHANG, J. 1999. The application of adaboost for distributed, scalable and on-line learning. In KDD '99: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM Press, New York, 362-366.
12. HANSEN, L. K. AND SALAMON, P. 1990. Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12, 10, 993-1001.
13. HITEC. http://categorizer.tmit.bmr.hu.
14. HOEL, P. G. 1966. Elementary Statistics, 2nd ed. Wiley, New York, NY.
15. HOWE, A. E. AND DREILINGER, D. 1997. SAVVYSEARCH: A metasearch engine that learns which search engines to query. AI Mag. 18, 2, 19-25.
16. JANSEN, B. J. 2000. The effect of query complexity on web searching results. Inf. Res. 6, 1.
17. JOACHIMS, T. 1998. Text categorization with suport vector machines: Learning with many relevant features. In ECML'98 Proceedings of the 10th European Conference on Machine Learning, 137-142.
18. JOACHIMS, T. 1999. Transductive inference for text classification using support vector machines. In ICML '99: Proceedings of the 16th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, 200-209.
19. JONES, K. S. 1971. Automatic Keyword Classifications for Information Retrieval. Butterworth, London, UK.
20. KANG, I.-H. AND KIM, G. 2003. 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. ACM Press, New York, 64-71.
21. KARDKOVÁCS, Z. T., TIKK, D., AND BÁNSÁGHI, Z. 2005. The ferrety algorithm for the kdd cup 2005 problem. SIGKDD Explor. Newsl. 7, 2, 111-116.
22. KITTLER, J., HATEF, M., DUIN, R. P. W., AND MATAS, J. 1998. On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20, 3, 226-239.
23. LEWIS, D.D. AND GALE, W. A. 1994. A sequential algorithm for training text classifiers. In SIGIR, W. B. Croft and C. J. van Rijsbergen, Eds. Springer Verlag, Berlin, Germany, 3-12.
24. Li, Y., ZHENG, Z., AND DAI, H. K. 2005. Kdd cup-2005 report: Facing a great challenge. SIGKDD Explor. Newsl. 7, 2, 91-99.
25. MCCALLUM, A. AND NIGAM, K. 1998. A comparison of event models for naive Bayes text classification. In Proceedings of the AAAI-98 Workshop on Learning for Text Categorization.
26. MEYER, D. A. AND BROWN, T. A. 1998. Statistical mechanics of voting. Phys. Revei. Lett. 81, 8, 1718-1721.
27. MILLER, G., BECKWITH, R., FELLBAUM, C., GROSS, D., AND MILLER, K. 1990. Introduction to wordnet: An on-line lexical database. Int. J. Lexicography 3, 4, 23-244.
28. PAGE, L., BRIN, S., MOTWANI, R., AND WINOGRAD, T. 1998. The pagerank citation ranking: Bringing order to the web. Tech. rep., Stanford Digital Library Technologies Project.
29. SELBERG, E. AND ETZIONI, O. 1995. Multi-service search and comparison using the MetaCrawler. In Proceedings of the 4th International World-Wide web Conference. Darmstadt, Germany.
30. SHEN, D., PAN, R., SUN, J.-T, PAN, J. J., Wu, K., YIN, J., AND YANG, Q. 2005. Q2c@ust: Our winning solution to query classification in kddcup 2005. SIGKDD Explor. Newsl. 7, 2, 100-110.
31. SHEN, D., SUN, J.-T, YANG, Q., AND CHEN, Z. 2006. Building bridges for web query classification. In SIGIR '06: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.
32. SILVEHSTEIN, C., MARAIS, H., HENZINGEH, M., AND MOHICZ, M. 1999. Analysis of a very large web search engine query log. SIGIR Forum. 33, 1, 6-12.
33. TIKK, D., BIRÓ, GY., AND YANG, J. D. 2005. Experiments with a hierarchial text categorization method on WIPO patent collections. In Applied Research in Uncertainty Modelling and Analysis, N. O. Attok-Okine and B. M. Ayyub, Eds. International Series in Intelligent Technologies, vol. 20, Springer-Verlag, 283-302.
34. VAN, R. C. 1979. Information Retrieval, 2nd ed. Butterworth, London, UK.
35. VOGEL, D., BICKEL, S., HAIDER, P., SCHIMPFKT, R., SIEMEN, P., BRIDGES, S., AND SCHEFFER, T. 2005. Classifying search engine queries using the web as background knowledge. SIGKDD Explor. Newsl. 7, 2, 117-122.
36. VOORHEES, E. M. 1994. Query expansion using lexical-semantic relations. In SIGIR '94: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Springer Verlag, Berlin, Germany. 61-69.
37. WEN, J.-R., NIE, J.-Y, AND ZHANG, H.-J. 2002. Query clustering using user logs. ACM Trans. Inf. Syst. 20, 1, 59-81.
38. YANG, Y. 1999. An evaluation of statistical approaches to text categorization. Inf. Retr. 1, 1-2, 69-90.
39. YANG, Y. AND PEDEHSEN, J. O. 1997. A comparative study on feature selection in text categorization. In ICML '97: Proceedings of the 14th International Conference on Machine Learning. Morgan Kaufmann, San Francisco, CA, 412-420.