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

Full metadata record

DC FieldValue Language
dc.contributor.authorYen, J.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorMa, P.en_US
dc.contributor.authorBui, T.en_US
dc.date.accessioned2004-09-09T00:00:01Z-
dc.date.available2010-06-18T23:27:00Z-
dc.date.issued1995en_US
dc.date.submitted2004-09-09en_US
dc.identifier.citationAn Issues Identifier for Online Financial Databases 1995,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105532-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractA 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherISDSSen_US
dc.subjectDatabasesen_US
dc.subjectInformation Extractionen_US
dc.subjectClassificationen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInformation retrievalen_US
dc.titleAn Issues Identifier for Online Financial Databasesen_US
dc.typeConference Paperen_US
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