An end-to-end learning solution for assessing the quality of Wikipedia articles

International audienceWikipedia is considered as the largest knowledge repository in the history of humanity and plays a crucial role in modern daily life. Assigning the correct quality class to Wikipedia articles is an important task in order to provide guidance for both authors and readers of Wikipedia. Manual review cannot cope with the editing speed of Wikipedia. An automatic classification is required to classify quality of Wikipedia articles. Most existing approaches rely on traditional machine learning with manual feature engineering, which requires a lot of expertise and effort. Furthermore, it is known that there is no general perfect feature set, because information leak always occurs in feature extraction phase. Also, for each language of Wikipedia a new feature set is required. In this paper, we present an approach relying on deep learning for quality classification of Wikipedia articles. Our solution relies on Recurrent Neural Networks (RNN) which is an end-to-end learning technique that eliminates disadvantages of feature engineering. Our approach learns directly from raw data without human intervention and is language-neutral.Experimental results on English, French and Russian Wikipedia datasets show that our approach outperforms state-of-the-art solutions

Automatically Assessing Wikipedia Article Quality by Exploiting Article–Editor Networks

Abstract. We consider the problem of automatically assessing Wikipedia article quality. We develop several models to rank articles by using the editing rela-tions between articles and editors. First, we create a basic model by modeling the article-editor network. Then we design measures of an editor’s contribution and build weighted models that improve the ranking performance. Finally, we use a combination of featured article information and the weighted models to obtain the best performance. We find that using manual evaluation to assist automatic eval-uation is a viable solution for the article quality assessment task on Wikipedia.