Measuring article quality in Wikipedia: Lexical clue model
|Measuring article quality in Wikipedia: Lexical clue model|
|Author(s)||Xu Y., Luo T.|
|Published in||IEEE Symposium on Web Society|
|Keyword(s)||article quality, decision tree, lexical clue, Wikipedia (Extra: High quality, lexical clue, Lexical features, Measuring method, Statistic feature, Wikipedia, Decision trees, Feature extraction, Forestry, Automation, Decision Theory, Experimentation, Labels, Mathematical Models, Quality)|
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Wikipedia is the most entry-abundant on-line encyclopedia. Some studies published by Nature proved that the scientific entries in Wikipedia are of good quality comparable to those in the Encyclopedia Britannica which are mainly maintained by experts. But the manual partition of the articles in Wikipedia from a WikiProject implies that high-quality articles are usually reached grade by grade via being repeatedly revised. So many work address to automatically measuring the article quality in Wikipedia based on some assumption of the relationship between the article quality and contributors' reputations, view behaviors, article status, inter-article link, or so on. In this paper, a lexical clue based measuring method is proposed to assess article quality in Wikipedia. The method is inspired the idea that the good articles have more regular statistic features on lexical usage than the primary ones due to the more revise by more people. We select 8 lexical features derived from the statistic on word usages in articles as the factors that can reflect article quality in Wikipedia. A decision tree is trained based on the lexical clue model. Using the decision tree, our experiments on a well-labeled collection of 200 Wikipedia articles shows that our method has more than 83% precise and recall.
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