Enweiji, MusbahLehinevych, TarasGlybovets, Andrii2018-07-022018-07-022017Enweiji M. Z. Cross-language text classification with convolutional neural networks from scratch / Musbah Zaid Enweiji, Taras Lehinevych, Аndrey Glybovets // EUREKA: Physics and Engineering. - 2017. - № 2. - С. 24-33.2461-4254https://ekmair.ukma.edu.ua/handle/123456789/13465http://dx.doi.org/10.21303/2461-4262.2017.00304Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach.entext classificationconvolutional neural networkcross-language text classificationmultilingual classificationtransfer learninginductive transfersupervised learningartificial neural networkarticleArticle