A two-stage text mining model for information filtering

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2008, Pages 1023-1032

  Li, Yuefeng ^a   Zhou, Xujuan ^a   Bruza, Peter ^a   Xu, Yue ^a   Lau, Raymond Y K ^b  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Decision rules; Information filtering; Text mining; Thresholds; Weighting schema

Indexed keywords

DECISION RULES; INFORMATION FILTERING; TEXT MINING; THRESHOLDS; WEIGHTING SCHEMA;

FILTRATION; KNOWLEDGE MANAGEMENT; MINING; TAXONOMIES;

INFORMATION RETRIEVAL SYSTEMS;

EID: 70349260848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1458082.1458218     Document Type: Conference Paper

References (33)

1. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1999.
2. N. J. Belkin and W. B. Croft. Information filtering and information retrieval: two sides of the same coin? Commun. ACM, 35(12):29-38, 1992.
3. R. Feldman and H. Hirsh. Mining associations in text in the presence of background knowledge. In Knowledge Discovery and Data Mining, pages 343-346, 1996.
4. J. D. Holt and S. M. Chung. Multipass algorithms for mining association rules in text databases. Knowl. Inf. Syst., 3(2):168-183, 2001.
5. N. Jindal and B. Liu. Identifying comparative sentences in text documents. In SIGIR, pages 244-251, 2006.
6. K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: development and comparative experiments - part 1. Inf. Process. Manage., 36(6):779-808, 2000.
7. K. S. Jones, S. Walker, and S. E. Robertson. A probabilistic model of information retrieval: development and comparative experiments - part 2. Inf. Process. Manage., 36(6):809-840, 2000.
8. R. Y. K. Lau, P. Bruza, and D. Song. Belief revision for adaptive information retrieval. In SIGIR, pages 130-137, 2004.
9. D. D. Lewis. An evaluation of phrasal and clustered representations on a text categorization task. In SIGIR, pages 37-50, 1992.
10. X. Li and B. Liu. Learning to classify texts using positive and unlabeled data. In IJCAI, pages 587-594, 2003.
11. Y. Li, C. Zhang, and J. R. Swan. An information filtering model on the web and its application in jobagent. Knowl.-Based Syst., 13(5):285-296, 2000.
12. Y. Li and N. Zhong. Web mining model and its applications for information gathering. Knowledge-Based Systems, 17:207-217, 2004.
13. Y. Li and N. Zhong. Mining ontology for automatically acquiring web user information needs. IEEE Transactions on Knowledge and Data Engineering, 18(4):554-568, 2006.
14. Y. Li and Y. Y. Yao. User profile model: a view from Artificial Intelligence. In 3rd International Conference on Rough Sets and Current Trends in Computing, Springer, Lecture Notes in Artificial Intelligence 2475, pages 493-496, 2002.
15. B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Effective personalization based on association rule discovery from web usage data. In Web Information and Data Management, pages 9-15, 2001.
16. J. Mostafa and W. Lam. Automatic classification using supervised learning in a medical document filtering application. Inf. Process. Manage., 36(3):415-444, 2000.
17. J. Mostafa, S. Mukhopadhyay, W. Lam, and M. J. Palakal. A multilevel approach to intelligent information filtering: Model, system, and evaluation. ACM Trans. Inf. Syst., 15(4):368-399, 1997.
18. T. Qin, X.-D. Zhang, D.-S. Wang, T.-Y. Liu, W. Lai, and H. Li. Ranking with multiple hyperplanes. In SIGIR, pages 279-286, 2007.
19. S. E. Robertson and I. Soboroff. The trec 2002 filtering track report. In TREC, 2002.
20. S. E. Robertson, S. Walker, and M. Hancock-Beaulieu. Okapi at trec-7: Automatic ad hoc, filtering, vlc and interactive. In TREC, pages 199-210, 1998.
21. S. E. Robertson, H. Zaragoza, and M. J. Taylor. Simple bm25 extension to multiple weighted fields. In CIKM, pages 42-49, 2004.
22. J. Rocchio. Relevance feedback in information retrieval, volume In The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice Hall, 1971.
23. S. Scott and S. Matwin. Feature engineering for text classification. In The 16th International Conference on Machine Learning, pages 379-388, 1999. Scott, Sam and Matwin, Stan.
24. F. Sebastiani. Machine learning in automated text categorization. ACM Comput. Surv., 34(1):1-47, 2002.
25. I. Soboroff and S. Robertson. Building a filtering test collection for trec 2002. In SIGIR, pages 243-250, New York, NY, USA, 2003.
26. S.T.Wu, Y.Li, Y. Xu, B. Pham, and P.Chen. Automatic pattern-taxonomy extraction for web mining. In the IEEE/WIC/ACM International Conference on Web Intelligence, pages 242 - 248, China, 2004.
27. S.-T. Wu, Y. Li, and Y. Xu. Deploying approaches for pattern refinement in text mining. In ICDM, pages 1157-1161, 2006.
28. Y. Xu and Y. Li. Generating concise association rules. In CIKM, pages 781-790, 2007.
29. T. W. Yan and H. Garcia-Molina. Sift - a tool for wide-area information dissemination. In USENIX Winter, pages 177-186, 1995.
30. Y. Yang, A. Lad, N. Lao, A. Harpale, B. Kisiel, and M. Rogati. Utility-based information distillation over temporally sequenced documents. In SIGIR, pages 31-38, 2007.
31. Y. Yang and X. Liu. A re-examination of text categorization methods. In SIGIR, pages 42-49, 1999.
32. Y. Y. Yao and S. K. M. Wong. A decision theoretic framework for approximating concepts. International Journal of Man-Machine Studies, 37(6):793-809, 1992.
33. Y. Y. Yao. Probabilistic rough set approximations International. to appear in Journal of Approximate Reasoning.