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dc.contributor.authorChen, Hsinchun*
dc.contributor.authorHsu, P.*
dc.contributor.authorOrwig, Richard E.*
dc.contributor.authorHoopes, L.*
dc.contributor.authorNunamaker, Jay F.*
dc.date.accessioned2004-10-01T00:00:01Z
dc.date.available2010-06-18T23:39:28Z
dc.date.issued1994-10en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationAutomatic Concept Classification of Text From Electronic Meetings 1994-10, 37(10):56-73 Communications of the ACMen_US
dc.identifier.urihttp://hdl.handle.net/10150/106084
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractIn this research we adopted an artificial intelligence (AI) approach to designing an automatic concept classification tool for electronic brainstorming output. The role of AI techniques such as machine learning and neural networks computing in groupware development can be significant. Through extensive content analysis, concept space generation, and neural network-based concept classification, our system can generate a tentative list of the important ideas and topics represented in meeting comments. Participants then can examine the systemâ s suggested list and the underlying comments. They can also revise or augment the list to produce their final consensus list. Allowing the system to act as an â intelligentâ aide for idea organization can alleviate some of the burdens of convergent tasks.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherACMen_US
dc.subjectManagement Information Systemsen_US
dc.subjectArtificial Intelligenceen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleAutomatic Concept Classification of Text From Electronic Meetingsen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalCommunications of the ACMen_US
refterms.dateFOA2018-06-23T22:00:28Z
html.description.abstractIn this research we adopted an artificial intelligence (AI) approach to designing an automatic concept classification tool for electronic brainstorming output. The role of AI techniques such as machine learning and neural networks computing in groupware development can be significant. Through extensive content analysis, concept space generation, and neural network-based concept classification, our system can generate a tentative list of the important ideas and topics represented in meeting comments. Participants then can examine the systemâ s suggested list and the underlying comments. They can also revise or augment the list to produce their final consensus list. Allowing the system to act as an â intelligentâ aide for idea organization can alleviate some of the burdens of convergent tasks.


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