Improving classification accuracy using automatically extracted training data

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2009, Pages 1145-1153

  Fuxman, Ariel ^a   Kannan, Anitha ^a   Goldberg, Andrew B ^b   Agrawal, Rakesh ^a   Tsaparas, Panayiotis ^a   Shafer, John ^a  

^a MICROSOFT RESEARCH   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION MODELS; DATA SOURCE; HIGH QUALITY; KNOWLEDGE DISCOVERY AND DATA MININGS; LABELED TRAINING DATA; LOW COSTS; LOWER COST; NATURAL LANGUAGES; RESEARCH EFFORTS; SEARCH QUERIES; SIMPLE ALGORITHM; SPECIFIC TASKS; TEXT CORPORA; TOOLBARS; TRAINING DATA; WIKIPEDIA;

MINING; NATURAL LANGUAGE PROCESSING SYSTEMS;

PARALLEL ALGORITHMS;

EID: 70350637651     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557143     Document Type: Conference Paper

References (17)

1. R. Agrawal, A. Halverson, K. Kenthapandi, N. Mishra, and P. Tsaparas. Generating labels from clicks. In WSDM, pages 172-181, 2009.
2. G. Andrew and J. Gao. Scalable training of l1-regularized log-linear models. In International Conference on Machine Learning, pages 33-40, 2007.
3. R. A. Baeza-Yates, L. Calderón-Benavides, and C. N. González-Caro. The intention behind web queries. In SPIRE, pages 98-109, 2006.
4. M. Banko and E. Brill. Scaling to very very large corpora for natural language disambiguation. In Proceedings of the Association for Computational Linguistics, pages 26-33, 2001.
5. S. Beitzel, E. Jensen, O. Frieder, and D. Grossman. Automatic web query classification using labeled and unlabeled training data. In In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 581-582. ACM Press, 2005.
6. T. Cover and J. Thomas. Elements of information theory. Wiley New York, 1991.
7. H. Dai, Z. Nie, L. Wang, L. Zhao, J. Wen, and Y. Li. Detecting online commercial intention. In In Proceedings of the 15th International World Wide Web Conference (WWW-06), pages 829-837, 2006.
8. T. Joachims. Optimizing search engines using clickthrough data. In KDD, pages 133-142. ACM, 2002.
9. T. Joachims, L. A. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay. Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Trans. Inf. Syst, 25(2), 2007.
10. M. Lapata and F. Keller. Web-based models for natural language processing. ACM Transactions on Speech and Language Processing, 1(1):1-31, 2005.
11. X. Li, Y. Wang, and A. Acero. Learning query intent from regularized click graphs. In ACM SIGIR conference on Research and Development in Information Retrieval, pages 339-346, 2008.
12. F. Muhlenbach, S. Lallich, and D. A. Zighed. Identifying and handling mislabelled instances. Journal of Intelligent Information Systems, 22(1):89-109, 2004.
13. P. Nakov and M. A. Hearst. Using the web as an implicit training set: Application to structural ambiguity resolution. In HLT/EMNLP, pages 835-842, 2005.
14. U. Rebbapragada and C. E. Brodley. Class noise mitigation through instance weighting. In ECML '07: Proceedings of the 18th European conference on Machine Learning, pages 708-715, Berlin, Heidelberg, 2007. Springer-Verlag.
15. D. Yarowsky. Unsupervised word sense disambiguation rivaling supervised methods. In In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 189-196, 1995.
16. X. Zeng and T. R. Martinez. A noise filtering method using neural networks. In Proceedings of the International Workshop of Soft Computing Techniques in Instrumentation, Measurement and Related Applications, pages 26-31, 2003.
17. X. Zhu. Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison, 2005.