Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms

Persistent Link:
http://hdl.handle.net/10150/106427
Title:
Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms
Author:
Chen, Hsinchun
Citation:
Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms 1995-04, 46(3):194-216 Journal of the American Society for Information Science
Publisher:
Wiley Periodicals, Inc
Journal:
Journal of the American Society for Information Science
Issue Date:
Apr-1995
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/106427
Submitted date:
2004-10-01
Abstract:
Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence; Indexing; Information Extraction
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Information retrieval

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-10-01T00:00:01Z-
dc.date.available2010-06-18T23:46:18Z-
dc.date.issued1995-04en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationMachine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms 1995-04, 46(3):194-216 Journal of the American Society for Information Scienceen_US
dc.identifier.urihttp://hdl.handle.net/10150/106427-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectIndexingen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInformation retrievalen_US
dc.titleMachine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithmsen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Scienceen_US
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