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

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

Authors: Quang-Vinh Dang:University de Lorraine; Claudia-Lavinia Ignat:INRIA

Abstract: Wikipedia 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. The manual review cannot cope with the editing speed of Wikipedia. An automatic classification is required to classify the 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.

Download: This contribution is part of the OpenSym 2017 proceedings and is available as a PDF file.

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