Re-ranking model based on document clusters

Information Processing and Management

Volumn 37, Issue 1, 2001, Pages 1-14

  Lee, Kyung Soon ^a   Park, Young Chan ^b   Choi, Key Sun ^a  

^a Division of Computer Science   (South Korea)

^b K4 M (Knowledge the New Millennium)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; DATA STRUCTURES; MATHEMATICAL MODELS; QUERY LANGUAGES;

INVERTED-FILE METHOD; RE-RANKING MODELS;

INFORMATION RETRIEVAL SYSTEMS;

EID: 0035153181     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0306-4573(00)00017-0     Document Type: Article

References (17)

1. Allan J. Relevance feedback with too much data. Proceedings of 18th ACM SIGIR International Conference on Research and Development in Information Retrieval. 1995;337-343.
2. Anick P.G., Vaithyanathan S. Exploiting clustering and phrases for context-based information retrieval. Proceedings of 20th ACM SIGIR International Conference on Research and Development in Information Retrieval. 1997;314-323.
3. Buckley C., Salton G., Allan J. The effect of adding relevance information in a relevance feedback environment. Proceedings of 17th ACM SIGIR International Conference on Research and Development in Information Retrieval. 1994;292-298.
4. Deerwester S., Dumais S.T., Harshman R. Indexing by latent semantic analysis. Journal of the American Society for Information Science. 41:(6):1990;391-407.
5. Eguchi K. Adaptive cluster-based browsing using incrementally expanded queries and its effects. Proceedings of 22nd ACM SIGIR International Conference on Research and Development in Information Retrieval. 1999;265-266.
6. Fitzpatrick L., Dent M. Automatic feedback using past queries: social searching? Proceedings of 20th ACM SIGIR International Conference on Research and Development in Information Retrieval. 1997;306-313.
7. Frakes W.B., Baeza-Yates R. Information retrieval: data structures & algorithms. 1992;435-436 Prentice Hall, New Jersey.
8. Hearst M.A., Pedersen J.O. Re-examining the cluster hypothesis: scatter/gather on retrieval results. Proceedings of 19th ACM SIGIR International Conference on Research and Development in Information Retrieval. 1996;76-84.
9. Kang, H. K. (1997). Two-level document ranking methods using mutual information in natural language information retrieval. Ph.D. thesis, Department of Computer Science, Korea Advanced Institute of Science and Technology.
10. Kemong. The Kemong Company new encyclopedia. 1992;Kemongsa Publishing Co, Seoul.
11. Lee J.H., Ahn J.S. Using n-grams for Korean text retrieval. Proceedings of 19th ACM SIGIR International Conference on Research and Development in Information Retrieval. 1996;216-224.
12. Mauldin M.L., Carbonell J.G. Conceptual information retrieval: a case study in adaptive partial parsing. 1991;Boston, Dordrecht, London, Kluwer Academic Publishers.
13. Murtagh F. A survey of recent advances in hierarchical clustering algorithms. Computer Journal. 26:1983;354-359.
14. Park, Y. C. (1997). Building word knowledge for information retrieval using statistical information. Ph.D. thesis, Department of Computer Science, Korea Advanced Institute of Science and Technology.
15. van Rijsbergen C.J. Information retrieval. 2nd ed. 1979;Butterworths, London.
16. Ward J.H. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association. 58:(301):1963;235-244.
17. Xu J., Croft W.B. Query expansion using local and global. Proceedings of 19th ACM SIGIR International Conference on Research and Development in Information Retrieval. 1996;4-11.