Quality Assessment of Wikipedia Articles without Feature Engineering

Quang-Vinh Dang 1, * Claudia-Lavinia Ignat 1
* Auteur correspondant
1 COAST - Web Scale Trustworthy Collaborative Service Systems
Inria Nancy - Grand Est, LORIA - NSS - Department of Networks, Systems and Services
Abstract : As Wikipedia became the largest human knowledge repository , quality measurement of its articles received a lot of attention during the last decade. Most research efforts fo-cused on classification of Wikipedia articles quality by using a different feature set. However, so far, no " golden feature set " was proposed. In this paper, we present a novel approach for classifying Wikipedia articles by analysing their content rather than by considering a feature set. Our approach uses recent techniques in natural language processing and deep learning, and achieved a comparable result with the state-of-the-art.
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Communication dans un congrès
Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, Jun 2016, Newark, United States. ACM, pp.27-30, 2016, 〈10.1145/2910896.2910917〉
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Quang-Vinh Dang, Claudia-Lavinia Ignat. Quality Assessment of Wikipedia Articles without Feature Engineering. Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, Jun 2016, Newark, United States. ACM, pp.27-30, 2016, 〈10.1145/2910896.2910917〉. 〈hal-01351226〉

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