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Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/1018
Title: An issues identifier for on-line financial databases
Authors: Yen, Jerome
Chen, H.
Ma, Pai Chun
Bui, Tung X.
Keywords: Databases
Information retrieval
Concept classification
Neural network modeling
Cluster analysis
Issue Date: 1995
Citation: Proceedings of the Third International Conference on Decision Support Systems, Hong Kong, June 22-23, 1995, Hong Kong University of Science and Technology, Hong Kong, 1995, p. 121-134
Abstract: 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 form the on-line financial databases. The system was developed based on a number to techniques: automatic indexing, concept space generation, 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.
URI: http://hdl.handle.net/1783.1/1018
Appears in Collections:ISOM Conference Papers

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