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Learning and optimization of an aspect hidden markov model for query language model generation. (2007)
Conference Proceeding
HUANG, Q., SONG, D., RUGER, S. and BRUZA, P. 2007. Learning and optimization of an aspect hidden markov model for query language model generation. In Dominich, S. and Kiss, F. (eds.) Studies in theory of information retrieval: proceedings of the 1st Association of Computing Machinery Special Interest Group on Information Retrieval (ACM SIGIR) international conference on the theory of information retrieval (ICTIR'07), 18-20 October 2007, Budapest, Hungary. Budapest: Foundation for Information Society (INFOTA), pages 157-164.

The Relevance Model (RM) incorporates pseudo relevance feedback to derive query language model and has shown a good performance. Generally, it is based on uni-gram models of individual feedback documents from which query terms are sampled independent... Read More about Learning and optimization of an aspect hidden markov model for query language model generation..