Cluster-based retrieval using language models
Top Cited Papers
- 25 July 2004
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 186-193
- https://doi.org/10.1145/1008992.1009026
Abstract
Previous research on cluster-based retrieval has been inconclusive as to whether it does bring improved retrieval effectiveness over document-based retrieval. Recent developments in the language modeling approach to IR have motivated us to re-examine this problem within this new retrieval framework. We propose two new models for cluster-based retrieval and evaluate them on several TREC collections. We show that cluster-based retrieval can perform consistently across collections of realistic size, and significant improvements over document-based retrieval can be obtained in a fully automatic manner and without relevance information provided by human.Keywords
This publication has 20 references indexed in Scilit:
- Passage retrieval based on language modelsPublished by Association for Computing Machinery (ACM) ,2002
- The effectiveness of query-specific hierarchic clustering in information retrievalInformation Processing & Management, 2002
- A probabilistic model of information retrieval: development and comparative experimentsInformation Processing & Management, 2000
- A probabilistic model of information retrieval: development and comparative experimentsInformation Processing & Management, 2000
- Comparison of Hierarchic Agglomerative Clustering Methods for Document RetrievalThe Computer Journal, 1989
- Recent trends in hierarchic document clustering: A critical reviewInformation Processing & Management, 1988
- Using interdocument similarity information in document retrieval systemsJournal of the American Society for Information Science, 1986
- A model of cluster searching based on classificationInformation Systems, 1980
- Document clustering: An evaluation of some experiments with the cranfield 1400 collectionInformation Processing & Management, 1975
- The use of hierarchic clustering in information retrievalInformation Storage and Retrieval, 1971