RUBRIC: A System for Rule-Based Information Retrieval
IEEE Transactions on Software Engineering - Special issue on COMPSAC 1982 and 1983
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Experiments with document components for indexing and retrieval
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Information Processing and Management: an International Journal - Modelling vagueness and subjectivity in information access
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Filtering search results using an optimal set of terms identified by an artificial neural network
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A risk minimization framework for information retrieval
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
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In this article we show how probabilistic information retrieval based on document components may be implemented as a feedforward (feedbackward) artificial neural network. The network supports adaptation of connection weights as well as the growing of new edges between queries and terms based on user relevance feedback data for training, and it reflects query modification and expansion in information retrieval. A learning rule is applied that can also be viewed as supporting sequential learning using a harmonic sequence learning rate. Experimental results with four standard small collections and a large Wall Street Journal collection (173,219 documents) show that performance of feedback improves substantially over no feedback, and further gains are obtained when queries are expanded with terms from the feedback documents. The effect is much more pronounced in small collections than in the large collection. Query expansion may be considered as a tool for both precision and recall enhancement. In particular, small query expansion levels of about 30 terms can achieve most of the gains at the low-recall high-precision region, while larger expansion levels continue to provide gains at the high-recall low-precision region of a precision recall curve.