Neural net learning issues in classification of free text documents

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In intelligent analysis of large amounts of text, not any single clue indicates reliably that a pattern of interest has been found. When using multiple clues, it is not known how these should be integrated into a decision. In the context of this investigation, we have been using neural nets as parameterized mappings that allow for fusion of higher level clues extracted from free text. By using higher level clues and features, we avoid very large networks. By using the dominant singular values computed by Latent Semantic Indexing (LSI) and applying neural network algorithms for integrating these values and the ... continued below

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4 p.

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Dasigi, V. R. & Mann, R. C. March 1, 1996.

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  • Dasigi, V. R. Sacred Heart Univ., Santurce (Puerto Rico). Dept. of Computer Science and Information Technology
  • Mann, R. C. Oak Ridge National Lab., TN (United States)

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Description

In intelligent analysis of large amounts of text, not any single clue indicates reliably that a pattern of interest has been found. When using multiple clues, it is not known how these should be integrated into a decision. In the context of this investigation, we have been using neural nets as parameterized mappings that allow for fusion of higher level clues extracted from free text. By using higher level clues and features, we avoid very large networks. By using the dominant singular values computed by Latent Semantic Indexing (LSI) and applying neural network algorithms for integrating these values and the outputs from other ``sensors,`` we have obtained preliminary encouraging results with text classification.

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4 p.

Notes

OSTI as DE96006711

Source

  • American Association for Artificial Intelligence (AAAI) spring symposium, Palo Alto, CA (United States), 25-27 Mar 1996

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  • Other: DE96006711
  • Report No.: CONF-9603127--1
  • Grant Number: AC05-96OR22464
  • Office of Scientific & Technical Information Report Number: 212422
  • Archival Resource Key: ark:/67531/metadc672701

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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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Creation Date

  • March 1, 1996

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • Oct. 3, 2017, 1:35 p.m.

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Dasigi, V. R. & Mann, R. C. Neural net learning issues in classification of free text documents, article, March 1, 1996; Tennessee. (digital.library.unt.edu/ark:/67531/metadc672701/: accessed April 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.