Difference between revisions of "Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling"
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== Overview == | == Overview == | ||
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, authors propose a novel Web-based shallow syntactic-semantic 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, authors have achieved high F-[[measures]] using logistic boosting and logistic model trees classifiers, surpassing the results reported by major Wikipedia vandalism detection systems. | 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, authors propose a novel Web-based shallow syntactic-semantic 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, authors 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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| + | == Embed == | ||
| + | === Wikipedia Quality === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Wang, William Yang; McKeown, Kathleen R.. (2010). "[[Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling]]". Association for Computational Linguistics. DOI: 10.7916/D8KP89J4. | ||
| + | </nowiki> | ||
| + | </code> | ||
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| + | === English Wikipedia === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | {{cite journal |last1=Wang |first1=William Yang |last2=McKeown |first2=Kathleen R. |title=Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling |date=2010 |doi=10.7916/D8KP89J4 |url=https://wikipediaquality.com/wiki/Got_You!:_Automatic_Vandalism_Detection_in_Wikipedia_with_Web-Based_Shallow_Syntactic-Semantic_Modeling |journal=Association for Computational Linguistics}} | ||
| + | </nowiki> | ||
| + | </code> | ||
| + | |||
| + | === HTML === | ||
| + | <code> | ||
| + | <nowiki> | ||
| + | Wang, William Yang; McKeown, Kathleen R.. (2010). &quot;<a href="https://wikipediaquality.com/wiki/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</a>&quot;. Association for Computational Linguistics. DOI: 10.7916/D8KP89J4. | ||
| + | </nowiki> | ||
| + | </code> | ||
Revision as of 00:01, 27 February 2021
Authors | William Yang Wang Kathleen R. McKeown |
|---|---|
Publication date | 2010 |
DOI | 10.7916/D8KP89J4 |
Links | Original |
Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling - scientific work related to Wikipedia quality published in 2010, written by William Yang Wang and Kathleen R. McKeown.
Overview
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, authors propose a novel Web-based shallow syntactic-semantic 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, authors have achieved high F-measures using logistic boosting and logistic model trees classifiers, surpassing the results reported by major Wikipedia vandalism detection systems.
Embed
Wikipedia Quality
Wang, William Yang; McKeown, Kathleen R.. (2010). "[[Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling]]". Association for Computational Linguistics. DOI: 10.7916/D8KP89J4.
English Wikipedia
{{cite journal |last1=Wang |first1=William Yang |last2=McKeown |first2=Kathleen R. |title=Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling |date=2010 |doi=10.7916/D8KP89J4 |url=https://wikipediaquality.com/wiki/Got_You!:_Automatic_Vandalism_Detection_in_Wikipedia_with_Web-Based_Shallow_Syntactic-Semantic_Modeling |journal=Association for Computational Linguistics}}
HTML
Wang, William Yang; McKeown, Kathleen R.. (2010). "<a href="https://wikipediaquality.com/wiki/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</a>". Association for Computational Linguistics. DOI: 10.7916/D8KP89J4.
