SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A cooccurrence-based thesaurus and two applications to information retrieval
Information Processing and Management: an International Journal
Textual context analysis for information retrieval
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Combining multiple evidence from different types of thesaurus for query expansion
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
On Relevance, Probabilistic Indexing and Information Retrieval
Journal of the ACM (JACM)
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
An effective approach to document retrieval via utilizing WordNet and recognizing phrases
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
The TREC robust retrieval track
ACM SIGIR Forum
ACM SIGIR Forum
Argumentative feedback: a linguistically-motivated term expansion for information retrieval
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Selecting good expansion terms for pseudo-relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Selecting Effective Terms for Query Formulation
AIRS '09 Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology
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Automatic Query expansion is a well-known method to improve the performance of information retrieval systems. In this paper we have suggested information theoretic measures to improve efficiency of co-occurrence based automatic query expansion. We have used pseudo relevance feedback based local approach. The expansion terms were selected from the top N documents using co-occurrence based approach. They were then ranked using two different information theoretic approaches. First one is standard Kullback-Leibler divergence (KLD). As a second measure we have suggested use of a variant KLD. Experiments were performed on TREC-1 dataset. The result suggests that there is a scope of improving co-occurrence based query expansion by using information theoretic measures. Extensive experiments were done to select two important parameters: number of top N documents to be used and number of terms to be used for expansion.