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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
A semantic vector space and features-based approach for automatic information filtering
Expert Systems with Applications, Volume 26, No. 2, Year 2004
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Description
With advances in communication technology, the amount of electronic information available to the users will become increasingly important. Users are facing increasing difficulties in searching and extracting relevant and useful information. Obviously, there is a strong demand for building automatic tools that capture, filter, control and disseminate the information that will most likely match a user's interest. In this paper we propose two kinds of knowledge to improve the efficiency of information filtering process. A features-based model for representing, evaluating and classifying texts. A semantic vector space to complement the features-based model on taking into account the semantic aspect. We used a neural network to model the user's interests (profiles) and a set of genetic algorithms for the learning process to improve filtering quality. To show the efficacy of such knowledge to deal with information filtering problem, particularly we present an intelligent and dynamic email filtering tool. It assists the user in managing, selecting, classifying and discarding non-desirable messages in a professional or non-professional context. The modular structure makes it portable and easy to adapt to other filtering applications such as the web browsing. We illustrate and discuss the system performance by experimental evaluation results. © 2003 Elsevier Ltd. All rights reserved.
Authors & Co-Authors
Nouali, Omar
Algeria, Ben Aknoun
Centre de Recherche Sur L'information Scientifique et Technique
France, Aix-en-provence
Laboratoire Parole et Langage
Blache, Philippe
France, Aix-en-provence
Laboratoire Parole et Langage
Statistics
Citations: 24
Authors: 2
Affiliations: 2
Identifiers
Doi:
10.1016/S0957-4174(03)00118-0
ISSN:
09574174
Research Areas
Genetics And Genomics