Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval based on context distance and morphology
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A vector space model for automatic indexing
Communications of the ACM
Placing search in context: the concept revisited
Proceedings of the 10th international conference on World Wide Web
Communications of the ACM
A context vector model for information retrieval
Journal of the American Society for Information Science and Technology
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ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
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ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Questioning query expansion: an examination of behaviour and parameters
ADC '04 Proceedings of the 15th Australasian database conference - Volume 27
Towards design principles for effective context- and perspective-based web mining
Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology
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DESRIST'10 Proceedings of the 5th international conference on Global Perspectives on Design Science Research
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This paper presents a novel context matching technique for the retrieval of web documents. The aim of the technique is to dynamically generate a contextbased measure of document term significance during retrieval that can be used as a substitute or cocontributor of the term frequency measure. Unlike term frequency, which relies on a term to occur multiple times within a document to be considered significant, context matching is based on the notion that if a term in a given document occurs in that document in the context of the query, then that term is deemed to be significant. Context matching has the ability to potentially determine a term to be significant even if it occurs only once in a large document. The proposed technique has been implemented and the experiments were conducted using a TREC benchmark database. A comparative analysis shows that context matching significantly improves retrieval effectiveness and outperforms previously published results.