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Probabilistically ranking web article quality based on evolution patterns
Abstract User-generated content (UGC) is created, uUser-generated content (UGC) is created, updated, and maintained by various web users, and its data quality is a major concern to all users. We observe that each Wikipedia page usually goes through a series of revision stages, gradually approaching a relatively steady quality state and that articles of different quality classes exhibit specific evolution patterns. We propose to assess the quality of a number of web articles using Learning Evolution Patterns (LEP). First, each article's revision history is mapped into a state sequence using the Hidden Markov Model (HMM). Second, evolution patterns are mined for each quality class, and each quality class is characterized by a set of quality corpora. Finally, an article's quality is determined probabilistically by comparing the article with the quality corpora. Our experimental results demonstrate that the LEP approach can capture a web article's quality precisely.capture a web article's quality precisely.
Abstractsub User-generated content (UGC) is created, uUser-generated content (UGC) is created, updated, and maintained by various web users, and its data quality is a major concern to all users. We observe that each Wikipedia page usually goes through a series of revision stages, gradually approaching a relatively steady quality state and that articles of different quality classes exhibit specific evolution patterns. We propose to assess the quality of a number of web articles using Learning Evolution Patterns (LEP). First, each article's revision history is mapped into a state sequence using the Hidden Markov Model (HMM). Second, evolution patterns are mined for each quality class, and each quality class is characterized by a set of quality corpora. Finally, an article's quality is determined probabilistically by comparing the article with the quality corpora. Our experimental results demonstrate that the LEP approach can capture a web article's quality precisely.capture a web article's quality precisely.
Bibtextype inproceedings  +
Doi 10.1007/978-3-642-34179-3_8  +
Has author Jangwhan Han + , Chen K. + , Jiang D. +
Has extra keyword ITS data + , Learning evolution + , State sequences + , User generated content + , Web users + , Wikipedia + , Expert systems + , Hidden Markov models +
Isbn 9783642341786  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 229–258  +
Published in Lecture Notes in Computer Science +
Title Probabilistically ranking web article quality based on evolution patterns +
Type conference paper  +
Volume 7600 LNCS  +
Year 2012 +
Creation dateThis property is a special property in this wiki. 7 November 2014 20:56:46  +
Categories Publications without keywords parameter  + , Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 7 November 2014 20:56:46  +
DateThis property is a special property in this wiki. 2012  +
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