Artificial Intelligence Lab, Department of MIS, University of Arizona / A major problem that decision makers are facing in an information-rich society is how to absorb, filter and make effective use of available data. The problem caused by information overflow could lead to the losses of competitiveness. This paper presents a knowledge-based approach to building an issues identifier to help investors
overcome information overflow problems when dealing with very large on-line financial databases. The proposed software system is able to extract critical issues from the on-line financial databases. The system was developed based on a number of techniques: automatic indexing, concept space genemtion, and neural network classification. In this paper, we describe how these techniques are used to extract subject descriptors, their semantic relationships, and the related texts (documents
or paragraphs) to each descriptor. The proposed system has been tested with the annual reports from thirteen of the largest international banks.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105532 |
| Date | January 1995 |
| Creators | Yen, J., Chen, Hsinchun, Ma, P., Bui, T. |
| Publisher | ISDSS |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
Page generated in 0.0015 seconds