Toward a multi-sensor neural net approach to automatic text classification

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Many automatic text indexing and retrieval methods use a term-document matrix that is automatically derived from the text in question. Latent Semantic Indexing, a recent method for approximating large term-document matrices, appears to be quite useful in the problem of text information retrieval, rather than text classification. Here we outline a method that attempts to combine the strength of the LSI method with that of neural networks, in addressing the problem of text classification. In doing so, we also indicate ways to improve performance by adding additional {open_quotes}logical sensors{close_quotes} to the neural network, something that is hard to do with ... continued below

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7 p.

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Dasigi, V. & Mann, R. January 26, 1996.

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This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 22 times , with 4 in the last month . More information about this article can be viewed below.

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  • Dasigi, V. Sacred Heart Univ., Fairfield, CT (United States). Department of Computer Science and Information Technology
  • Mann, R. Oak Ridge National Laboratory, TN (United States)

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Description

Many automatic text indexing and retrieval methods use a term-document matrix that is automatically derived from the text in question. Latent Semantic Indexing, a recent method for approximating large term-document matrices, appears to be quite useful in the problem of text information retrieval, rather than text classification. Here we outline a method that attempts to combine the strength of the LSI method with that of neural networks, in addressing the problem of text classification. In doing so, we also indicate ways to improve performance by adding additional {open_quotes}logical sensors{close_quotes} to the neural network, something that is hard to do with the LSI method when employed by itself. Preliminary results are summarized, but much work remains to be done.

Physical Description

7 p.

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OSTI as DE96008633

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  • International Federation of Information Processing (IFIP) world conference on advanced IT tools, intelligent systems track, Canberra (Australia), 2-6 Sep 1996

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  • Other: DE96008633
  • Report No.: CONF-9609113--1
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 266901
  • Archival Resource Key: ark:/67531/metadc671260

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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  • January 26, 1996

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  • June 29, 2015, 9:42 p.m.

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  • Jan. 22, 2016, 6:55 p.m.

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Dasigi, V. & Mann, R. Toward a multi-sensor neural net approach to automatic text classification, article, January 26, 1996; Tennessee. (digital.library.unt.edu/ark:/67531/metadc671260/: accessed October 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.