An adaptable connectionist text-retrieval system with relevance feedback Azimi-Sadjadi, Mahmood R. ; Salazar, Jaime ; Srinivasan, S. (Srini) ; Sheedvash, Sassan "This work was supported by the Hewlett Packard, Boise, ID management and business teams under Contract 50B000553." 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. Colorado State University. Libraries 2007 text ; image application/pdf ECEmra00062.pdf FACFECEN100542ARTI eng c2007 IEEE
An adaptable connectionist text-retrieval system with relevance feedback
Azimi-Sadjadi, Mahmood R. ; Salazar, Jaime ; Srinivasan, S. (Srini) ; Sheedvash, Sassan
"This work was supported by the Hewlett Packard, Boise, ID management and business teams under Contract 50B000553."
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.
Colorado State University. Libraries
2007
text ; image
application/pdf
ECEmra00062.pdf
FACFECEN100542ARTI
eng
c2007 IEEE