Information Retrieval System Using Latent Contextual Relevance

When the relevance feedback, which is one of the most popular information retrieval model, is used in an information retrieval system, a related word is extracted based on the first retrival result. Then these words are added into the original query, and retrieval is performed again using updated query. Generally, Using such query expansion technique, retrieval performance using the query expansion falls in comparison with the performance using the original query. As the cause, there is a few synonyms in the thesaurus and although some synonyms are added to the query, the same documents are retireved as a result. In this paper, to solve the problem over such related words, we propose latent context relevance in consideration of the relevance between query and each index words in the document set.