Towards detection of influential sentences affecting reputation in Wikipedia
Zhou, Yiwei; Cristea, A.I.
Wikipedia has become the most frequently viewed online encyclopaedia website. Some sentences in Wikipedia articles have direct and obvious impact on people's opinions towards the mentioned named entities. This paper defines and tackles the problem of reputation-influential sentence detection in Wikipedia articles from various domains. We leverage multiple lexicons, to generate domain independent features. We generate topical features and word embedding features from unlabelled dataset, to boost the classification performance. We conduct several experiments, to prove the effectiveness of these features. We further adapt a two-step binary classification method, to perform multi-classification. Our evaluation results show that this method outperforms the state-of-the-art one-vs-one multi-classification method for this problem.
Zhou, Y., & Cristea, A. (2016). Towards detection of influential sentences affecting reputation in Wikipedia. In W. Nejdl (Ed.), WebSci '16 : Proceedings of the 8th ACM Conference on Web Science (244-248). https://doi.org/10.1145/2908131.2908177
|Conference Name||ACM Web Science Conference 2016|
|Acceptance Date||Mar 23, 2016|
|Publication Date||Jan 1, 2016|
|Deposit Date||Jul 11, 2018|
|Publicly Available Date||Jul 31, 2018|
|Book Title||WebSci '16 : Proceedings of the 8th ACM Conference on Web Science|
|Related Public URLs||http://wrap.warwick.ac.uk/78605/|
Accepted Conference Proceeding
© 2016 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in WebSci '16 : Proceedings of the 8th ACM Conference on Web Science, http://dx.doi.org/10.1145/2908131.2908177
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