Information fusion for automatic text classification
- Department of Computer Science and Information Technology, Sacred Heart University, Fairfield, CT (United States)
- Computer and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN (United States)
Analysis and classification of free text documents encompass decision-making processes that rely on several clues derived from text and other contextual information. When using multiple clues, it is generally not known a priori how these should be integrated into a decision. An algorithmic sensor based on Latent Semantic Indexing (LSI) (a recent successful method for text retrieval rather than classification) is the primary sensor used in our work, but its utility is limited by the {ital reference}{ital library} of documents. Thus, there is an important need to complement or at least supplement this sensor. We have developed a system that uses a neural network to integrate the LSI-based sensor with other clues derived from the text. This approach allows for systematic fusion of several information sources in order to determine a combined best decision about the category to which a document belongs.
- Research Organization:
- Oak Ridge National Lab., TN (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 378178
- Report Number(s):
- CONF-9608120--1; ON: DE96013781
- Country of Publication:
- United States
- Language:
- English
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