Eliminating Redundant and Less-informative RSS News Articles Based on Word Similarity and A Fuzzy Equivalence Relation

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 regarding processing of online news feeds, i.e., 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 thesis, 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.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Garcia, Ian

• RSS news feeds similarity fuzzy-set redundant less-informative relation clusters
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/1054

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-2053