A Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation

Persistent Link:
http://hdl.handle.net/10150/106141
Title:
A Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation
Author:
Roussinov, Dmitri G.; Chen, Hsinchun
Citation:
A Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation 1998, 15(1-2):81-111 Communication and Cognition in Artificial Intelligence Journal
Journal:
Communication and Cognition in Artificial Intelligence Journal
Issue Date:
1998
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/106141
Submitted date:
2004-09-04
Abstract:
The rapid proliferation of textual and multimedia online databases, digital libraries, Internet servers, and intranet services has turned researchers' and practitioners' dream of creating an information-rich society into a nightmare of info-gluts. Many researchers believe that turning an info-glut into a useful digital library requires automated techniques for organizing and categorizing large-scale information. This paper presents research in which we sought to develop a scaleable textual classification and categorization system based on the Kohonen's self-organizing feature map (SOM) algorithm. In our paper, we show how self-organization can be used for automatic thesaurus generation. Our proposed data structure and algorithm took advantage of the sparsity of coordinates in the document input vectors and reduced the SOM computational complexity by several order of magnitude. The proposed Scaleable SOM (SSOM) algorithm makes large-scale textual categorization tasks a possibility. Algorithmic intuition and the mathematical foundation of our research are presented in detail. We also describe three benchmarking experiments to examine the algorithm's performance at various scales: classification of electronic meeting comments, Internet homepages, and the Compendex collection.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Knowledge Organization; Classification
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Evaluation

Full metadata record

DC FieldValue Language
dc.contributor.authorRoussinov, Dmitri G.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-09-04T00:00:01Z-
dc.date.available2010-06-18T23:41:32Z-
dc.date.issued1998en_US
dc.date.submitted2004-09-04en_US
dc.identifier.citationA Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generation 1998, 15(1-2):81-111 Communication and Cognition in Artificial Intelligence Journalen_US
dc.identifier.urihttp://hdl.handle.net/10150/106141-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThe rapid proliferation of textual and multimedia online databases, digital libraries, Internet servers, and intranet services has turned researchers' and practitioners' dream of creating an information-rich society into a nightmare of info-gluts. Many researchers believe that turning an info-glut into a useful digital library requires automated techniques for organizing and categorizing large-scale information. This paper presents research in which we sought to develop a scaleable textual classification and categorization system based on the Kohonen's self-organizing feature map (SOM) algorithm. In our paper, we show how self-organization can be used for automatic thesaurus generation. Our proposed data structure and algorithm took advantage of the sparsity of coordinates in the document input vectors and reduced the SOM computational complexity by several order of magnitude. The proposed Scaleable SOM (SSOM) algorithm makes large-scale textual categorization tasks a possibility. Algorithmic intuition and the mathematical foundation of our research are presented in detail. We also describe three benchmarking experiments to examine the algorithm's performance at various scales: classification of electronic meeting comments, Internet homepages, and the Compendex collection.en_US
dc.format.mimetypetext/htmlen_US
dc.language.isoenen_US
dc.subjectKnowledge Organizationen_US
dc.subjectClassificationen_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.otherEvaluationen_US
dc.titleA Scalable Self-organizing Map Algorithm for Textual Classification: A Neural Network Approach to Thesaurus Generationen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalCommunication and Cognition in Artificial Intelligence Journalen_US
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