Cross-language text classification with convolutional neural networks from scratch
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Date
2017
Authors
Enweiji, Musbah
Lehinevych, Taras
Glybovets, Andrii
Journal Title
Journal ISSN
Volume Title
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Abstract
Cross 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.
Description
Keywords
text classification, convolutional neural network, cross-language text classification, multilingual classification, transfer learning, inductive transfer, supervised learning, artificial neural network, article
Citation
Enweiji 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.