Cluster-based language models for distributed retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A general language model for information retrieval (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating document clustering for interactive information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Two-stage language models for information retrieval
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The effectiveness of query-specific hierarchic clustering in information retrieval
Information Processing and Management: an International Journal
Bayesian extension to the language model for ad hoc information retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
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We propose a new retrieval method based on combining language models with clustering. The basic idea of the method is as follows. Firstly, documents in the collection are grouped into clusters by using a clustering algorithm. Secondly, clusters are imported into building language models which are used to estimate how likely a query could be generated from them. Thirdly, language models are smoothed by using a two-stage smoothing method. Our experiments show that the method outperforms both approach “purely” based on clustering and technique “purely” based on language model.