A graphical self-organizing approach to classifying electronic meeting output

Persistent Link:
http://hdl.handle.net/10150/105681
Title:
A graphical self-organizing approach to classifying electronic meeting output
Author:
Orwig, Richard E.; Chen, Hsinchun; Nunamaker, Jay F.
Citation:
A graphical self-organizing approach to classifying electronic meeting output 1997-02, 48(2):157-170 Journal of the American Society for Information Science
Publisher:
Wiley Periodicals, Inc
Journal:
Journal of the American Society for Information Science
Issue Date:
Feb-1997
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105681
Submitted date:
2004-10-29
Abstract:
This article describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. This research builds upon previous work in automating the meeting classification process using a Hopfield neural network. Evaluation of the Kohonen output comparing it with Hopfield and human expert output using the same set of data found that the Kohonen SOM performed as well as a human expert in representing term association in the meeting output and outperformed the Hopfield neural network algorithm. Recall of consensus meeting concepts and topics using the Kohonen algorithm was equivalent to that of the human expert.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence; Knowledge Management
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab

Full metadata record

DC FieldValue Language
dc.contributor.authorOrwig, Richard E.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorNunamaker, Jay F.en_US
dc.date.accessioned2004-10-29T00:00:01Z-
dc.date.available2010-06-18T23:31:47Z-
dc.date.issued1997-02en_US
dc.date.submitted2004-10-29en_US
dc.identifier.citationA graphical self-organizing approach to classifying electronic meeting output 1997-02, 48(2):157-170 Journal of the American Society for Information Scienceen_US
dc.identifier.urihttp://hdl.handle.net/10150/105681-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThis article describes research in the application of a Kohonen Self-Organizing Map (SOM) to the problem of classification of electronic brainstorming output and an evaluation of the results. This research builds upon previous work in automating the meeting classification process using a Hopfield neural network. Evaluation of the Kohonen output comparing it with Hopfield and human expert output using the same set of data found that the Kohonen SOM performed as well as a human expert in representing term association in the meeting output and outperformed the Hopfield neural network algorithm. Recall of consensus meeting concepts and topics using the Kohonen algorithm was equivalent to that of the human expert.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectKnowledge Managementen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleA graphical self-organizing approach to classifying electronic meeting outputen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Scienceen_US
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