Repository logo
 

An adaptable connectionist text-retrieval system with relevance feedback

Simple item page
dc.contributor.authorSheedvash, Sassan, author
dc.contributor.authorSrinivasan, S. (Srini), author
dc.contributor.authorSalazar, Jaime, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:49:01Z
dc.date.available2007-01-03T04:49:01Z
dc.date.issued2007
dc.description.abstractThis 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.
dc.description.sponsorshipThis work was supported by the Hewlett Packard, Boise, ID management and business teams under Contract 50B000553.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationAzimi-Sadjadi, M. R., et al., An Adaptable Connectionist Text-Retrieval System with Relevance Feedback, IEEE Transactions on Neural Networks 18, no. 6 (November 2007): 1597-1613.
dc.identifier.urihttp://hdl.handle.net/10217/931
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2007 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectquery mapping
dc.subjectlearning algorithms
dc.subjectconnectionist networks
dc.subjectrelevance feedback
dc.subjecttext retrieval
dc.titleAn adaptable connectionist text-retrieval system with relevance feedback
dc.typeText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ECEmra00062.pdf
Size:
792.44 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Faculty Publications