Neural net learning issues in classification of free text documents
- Sacred Heart Univ., Santurce (Puerto Rico). Dept. of Computer Science and Information Technology
- Oak Ridge National Lab., TN (United States)
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.
- Research Organization:
- Oak Ridge National Lab., TN (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States); Oak Ridge Inst. for Science and Education, TN (United States)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 212422
- Report Number(s):
- CONF-9603127--1; ON: DE96006711
- Country of Publication:
- United States
- Language:
- English
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