"Got You!": Automatic vandalism detection in wikipedia with web-based shallow syntactic-semantic modeling
|"Got You!": Automatic vandalism detection in wikipedia with web-based shallow syntactic-semantic modeling|
|Author(s)||Wang W.Y., McKeown K.R.|
|Published in||Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference|
|Keyword(s)||Unknown (Extra: Detection system, Lexical features, Linguistic analysis, Logistic models, Modeling method, N-gram language models, Rule-based method, Web searches, Wikipedia, Computational linguistics, Semantic Web, Semantics, Syntactics, User interfaces, Websites)|
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"Got You!": Automatic vandalism detection in wikipedia with web-based shallow syntactic-semantic modeling is a 2010 conference paper written in English by Wang W.Y., McKeown K.R. and published in Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference.
Discriminating vandalism edits from non-vandalism edits in Wikipedia is a challenging task, as ill-intentioned edits can include a variety of content and be expressed in many different forms and styles. Previous studies are limited to rule-based methods and learning based on lexical features, lacking in linguistic analysis. In this paper, we propose a novel Web-based shallow syntacticsemantic modeling method, which utilizes Web search results as resource and trains topic-specific n-tag and syntactic n-gram language models to detect vandalism. By combining basic task-specific and lexical features, we have achieved high F-measures using logistic boosting and logistic model trees classifiers, surpassing the results reported by major Wikipedia vandalism detection systems.
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