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    An Issues Identifier for Online Financial Databases

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    Author
    Yen, J.
    Chen, Hsinchun
    Ma, P.
    Bui, T.
    Issue Date
    1995
    Submitted date
    2004-09-09
    Keywords
    Databases
    Information Extraction
    Classification
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    Information retrieval
    
    Metadata
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    Citation
    An Issues Identifier for Online Financial Databases 1995,
    Publisher
    ISDSS
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105532
    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 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.
    Type
    Conference Paper
    Language
    en
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    DLIST

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