Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and a Fuzzy Equivalence Relation

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Permanent URL	http://hdl.lib.byu.edu/1877/2631
Title	Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and a Fuzzy Equivalence Relation
BYU Creator	Garcia, Ian Ng, Yiu-Kai D.
Keywords	RSS feeds; fuzzy equivalence; filtering news articles
Description/Abstract	The Internet has marked this era as the information age. There is no precedent in the amazing amount of information, especially network news, that can be accessed by Internet users these days. As a result, the problem of seeking information in online news articles is not the lack of them but being overwhelmed by them. This brings huge challenges in processing online news feeds, e.g., how to determine which news article is important, how to determine the quality of each news article, and how to filter irrelevant and redundant information. In this paper, we propose a method for filtering redundant and less-informative RSS news articles that solves the problem of excessive number of news feeds observed in RSS news aggregators. Our filtering approach measures similarity among RSS news entries by using the Fuzzy-Set Information Retrieval model and a fuzzy equivalent relation for computing word/sentence similarity to detect redundant and less-informative news articles.
Source	Ian Gracia and Yiu-Kai Ng. "Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and A Fuzzy Equivalence Relation." In Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2006), pp. 465-473, November 13-15, 2006, Washington, D.C.;
Publisher	IEEE
Date Original	2006-11-13
Date Digital	2010-10-26
Language	English eng en
Publication Type	Articles Conference Proceedings
Type	Text
Format	application/pdf
Owning Institution	Brigham Young University
BYU College	College of Physical and Mathematical Sciences
BYU Department	Computer Science
Copyright Status	(c) 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.;
Copyright Use Information	http://www.lib.byu.edu/generic_copyright.html

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Eliminating Redundant and Less-Informative RSS News Articles Based on Word Similarity and a Fuzzy Equivalence Relation

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