An adaptable connectionist text-retrieval system with relevance feedback.
This paper introduces a new connectionist network for certain domain-specific text-retrieval and search applications with expert end users. A new model reference adaptive system is proposed that involves three learning phases. Initial model-reference learning is first performed based upon an ensemble set of input-output of an initial reference model. Model-reference following is needed in dynamic environments where documents are added, deleted, or updated. Relevance feedback learning from multiple expert users then optimally maps the original query using either a score-based or a click-through selection process. The learning can be implemented, in regression or classification modes, using a three-layer network. The first layer is an adaptable layer that performs mapping from query domain to document space. The second and third layers perform document-to-term mapping, search/retrieval, and scoring tasks. The learning algorithms are thoroughly tested on a domain-specific text database that encompasses a wide range of Hewlett Packard (HP) products and for a large number of most commonly used single- and multiterm queries.
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA. azimi@engr.colostate.edu
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Azimi-Sadjadi
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Salazar
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Srinivasan
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Sheedvash
D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D001330:Electronic Data Processing / Q000379:methods; D005103:Expert Systems; D005246:Feedback; D017143:Fuzzy Logic; D007256:Information Systems; D039703:Logical Observation Identifiers Names and Codes; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011381:Programming Languages; D012984:Software; D014584:User-Computer Interface