| Edgardo Ferretti|
(Alternative names for this author)
|Co-authors||Benno Stein, Cagnina L., Donato Hernández Fusilier, Horn C., Lex E., Manuel Montes y Gómez, Marcelo Errecalde, Michael Granitzer, Paolo Rosso, Rafael Guzmán Cabrera, Voelske M.|
|Authorship||Publications (2), datasets (0), tools (0)|
|Citations||Total (1), average (0.5), median (0.5), max (1), min (0)|
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PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|Measuring the quality of web content using factual information||ACM International Conference Proceeding Series||English||2012||Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With factual density we present a simple statistical quality measure that is based on facts extracted from Web content using Open Information Extraction. In a first case study, we use this measure to identify featured/good articles in Wikipedia. We compare the factual density measure with word count, a measure that has successfully been applied to this task in the past. Our evaluation corroborates the good performance of word count in Wikipedia since featured/good articles are often longer than non-featured. However, for articles of similar lengths the word count measure fails while factual density can separate between them with an F-measure of 90.4%. We also investigate the use of relational features for categorizing Wikipedia articles into featured/good versus non-featured ones. If articles have similar lengths, we achieve an F-measure of 86.7% and 84% otherwise.||0||0|
|On the Use of PU Learning for Quality Flaw Prediction in Wikipedia||PAN||English||2012||In this article we describe a new approach to assess Quality Flaw Prediction in Wikipedia. The partially supervised method studied, called PU Learning, has been successfully applied in classifications tasks with traditional corpora like Reuters-21578 or 20-Newsgroups. To the best of our knowledge, this is the first time that it is applied in this domain. Throughout this paper, we describe how the original PU Learning approach was evaluated for assessing quality flaws and the modifications introduced to get a quality flaws predictor which obtained the best F1 scores in the task “Quality Flaw Prediction in Wikipedia” of the PAN challenge.||0||1|