Title :
FIDS: an intelligent financial Web news articles digest system
Author :
Lam, Wai ; Ho, Kei Shiu
Author_Institution :
Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Shatin, China
fDate :
11/1/2001 12:00:00 AM
Abstract :
In this paper, we present a system called FIDS (Financial Information Digest System), which can digest online financial news automatically. Compared to previous approaches, FIDS is unique in the way that it can understand news articles in different domains simultaneously. These domains are all concerned with financial news. The system is able to integrate the information from different articles by conducting automatic content-based classification and information item extraction. Moreover, it allows one to perform cross-validation on their contents. As a result, users can have access to more complete information which otherwise would be scattered in different articles
Keywords :
abstracting; artificial intelligence; classification; financial data processing; full-text databases; information resources; natural languages; FIDS; Financial Information Digest System; automatic content-based classification; content cross-validation; information integration; information item extraction; intelligent financial Web news articles digest system; online financial news; simultaneous news article understanding; text summarization; Adaptive systems; Bayesian methods; Data mining; Explosions; Information filtering; Information filters; Internet; Learning systems; Scattering; Text categorization;
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
DOI :
10.1109/3468.983433
