Information retrieval as statistical translation
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Capturing term dependencies using a language model based on sentence trees
Proceedings of the eleventh international conference on Information and knowledge management
Example-based machine translation using efficient sentence retrieval based on edit-distance
ACM Transactions on Asian Language Information Processing (TALIP)
Probabilistic document-context based relevance feedback with limited relevance judgments
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
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A number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the most exact description of user's information needs. In this paper we interpret query context as a document consisting of sentences related to the current query. This kind of query context is used to re-estimate the relevance probabilities of top-ranked documents and then re-rank top-ranked documents. The experiments show that the proposed context-based approach for information retrieval can greatly improved relevance of search results.