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 ensembl...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2007-11, Vol.18 (6), p.1597-1613
Main Authors: Azimi-Sadjadi, M.R., Salazar, J., Srinivasan, S., Sheedvash, S.
Format: Article
Language:English
Subjects:
Abstracting and Indexing as Topic
Adaptive systems
Algorithms
Applied sciences
Artificial Intelligence
Automatic Data Processing - methods
Computer science
control theory
systems
Connectionist networks
Database Management Systems
Exact sciences and technology
Expert Systems
Feedback
Fuzzy Logic
Identity management systems
Information Systems
Information systems. Data bases
Learning
learning algorithms
Logical Observation Identifiers Names and Codes
Mapping
Memory organisation. Data processing
Navigation
Networks
Neural networks
Neural Networks (Computer)
Pattern Recognition, Automated - methods
Programming Languages
query mapping
Query processing
relevance feedback
Searching
Software
Studies
Support vector machines
Testing
text retrieval
User-Computer Interface
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Summary: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.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2007.895912