Difference between revisions of "Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling"
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| + | | title = Got You!: Automatic Vandalism Detection in Wikipedia with Web-Based Shallow Syntactic-Semantic Modeling | ||
| + | | date = 2010 | ||
| + | | authors = [[William Yang Wang]]<br />[[Kathleen R. McKeown]] | ||
| + | | doi = 10.7916/D8KP89J4 | ||
| + | | link = http://dl.acm.org/citation.cfm?id=1873781.1873910 | ||
| + | }} | ||
'''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]]. | '''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 == | == 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. | ||
Revision as of 01:07, 5 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.
