City Research Online

Abstract

At their heart, most if not all information retrieval models utilize some form of term frequency. The notion is that the more often a query term occurs in a document, the more likely it is that document meets an information need. We examine an alternative. We propose a model which assesses the presence of a term in a document not by looking at the actual occurrence of that term, but by a set of nonindependent supporting terms, i.e. context. This yields a weighting for terms in documents which is different from and complementary to tf-based methods, and is beneficial for retrieval.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	© A, MAcfarlane, ACM 2006. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management, http://dx.doi.org/10.1145/10.1145/1183614.1183694
Publisher Keywords:	Maximum entropy, conditional random fields, context-based retrieval
Subjects:	Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments:	School of Science & Technology > Computer Science > Human Computer Interaction Design

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