Coupling feature selection and machine learning methods for navigational query identification

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2006, Pages 682-689

  Lu, Yumao ^a   Peng, Fuchun ^a   Li, Xin ^a   Ahmed, Nawaaz ^a  

^a YAHOO INC   (United States)

Author keywords

Machine learning; Navigational query classification

Indexed keywords

ERROR RATE REDUCTION; NAVIGATIONAL QUERY CLASSIFICATION; UNCOUPLED SYSTEMS;

FEATURE EXTRACTION; GRADIENT METHODS; ONLINE SEARCHING; QUERY PROCESSING; RANDOM PROCESSES; WEB SERVICES;

SUPPORT VECTOR MACHINES;

EID: 34547633482     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1183614.1183711     Document Type: Conference Paper

References (19)

1. S. Beitzel, E. Jensen, D. Lewis, A. Chowdhury, A. Kolcz, and O. Frieder. Improving Automatic Query Classification via, Semi-supervised Learning. In The Fifth IEEE International Conference on Data Mining, pages 27-30, New Orleans, Louisiana, November 2005.
2. C. Bhattacharyya, L. R. Grate, M. I. Jordan, L. El Ghaoui, and I. S. Mian. Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data,. Journal of Computational Biology, 11(6):1073-1089, 2004.
3. A. Broder. A Taxonomy of Web Search. In ACM SIGIR Forum, pages 3-10, 2002.
4. S. della Pietra, V. della Pietra, and J. Lafferty. Inducing Features of Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4), 1995.
5. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley, New York, NY, 2nd edition, 2000.
6. J. H. Friedman. Stochastic Gradient Boosting. Computational Statistics and Data Analysis, 38(4):367-378, 2002.
7. L. Gravano, V. Hatzivassiloglou, and R. Lichtenstein. Categorizing Web Queries According to Geographical Locality. In ACM 12th Conference on Information and Knowledge Management (CIKM), pages 27-30, New Orleans, Louisiana, November 2003.
8. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Predication. Springer Verlag, New York, 2001.
9. E. T. Jaynes. Papers on Probability, Statistics, and Statistical Physics. D. Reidel, Dordrecht, Holland and Boston and Hingham, MA, 1983.
10. T. Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proceedings of the 10th European Conference on Machine Learning (ECML), pages 137-142, Chemnitz, Germany, 1998.
11. I.-H. Kang and G. Kim. Query Type Classification for Web Document Retrieval. In Proceedings of the 26th annual international A CM SIGIR conference on Research and development in informaion retrieval, pages 64 - 71, Toronto Canada, July 2003.
12. U. Lee, Z. Liu, and J. Cho. Automatic Identification of User Goals in Web Search. In Proceedings of the 14th International World Wide Web Conference (WWW), Chiba, Japan, 2005.
13. R. Malouf. A Comparison of Algorithms for Maximum Entropy Parameter Estimation. In Proceedings of the Sixth Conference on Natural Language Learning (CoNLL), Taipei, China, 2002.
14. D. E. Rose and D. Levinson. Understanding User Goals in Web Search. In Proceedings of The 13th International World Wide Web Conference (WWW), 2004.
15. D. Shen, R. Pan, J.-T. Sun, J. J. Pan, K. Wu, J. Yin, and Q. Yang. Q2C at UST: Our Winning Solution to Query Classification in KDDCUP 2005. SIGKDD Explorations, 7(2): 100-110, 2005.
16. L. Sherman and J. Deighton. Banner advertising: Measuring effectiveness and optimizing placement. Journal of Interactive Marketing, 15(2):60-64, 2001.
17. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.
18. Y. Yang and J. Pedersen. An Comparison Study on Feature Selection in Text Categorization. In Proceedings of the 20th annual international ACM SIGIR. conference on Research and development in informaion retrieval, Philadelphia, PA, USA, 1997.
19. S.C Zhu. Statistical modeling and conceptualization of visual patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(6):619-712, 2003.