Cross-language text classification with convolutional neural networks from scratch

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dc.contributor.authorEnweiji, Musbah
dc.contributor.authorLehinevych, Taras
dc.contributor.authorGlybovets, Andrii
dc.date.accessioned2018-07-02T11:45:01Z
dc.date.available2018-07-02T11:45:01Z
dc.date.issued2017
dc.description.abstractCross 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.en_US
dc.identifier.citationEnweiji 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.en_US
dc.identifier.issn2461-4254
dc.identifier.urihttps://ekmair.ukma.edu.ua/handle/123456789/13465
dc.identifier.urihttp://dx.doi.org/10.21303/2461-4262.2017.00304
dc.language.isoenen_US
dc.relation.sourceEUREKA: Physics and Engineering. - 2017. - № 2en_US
dc.statusfirst publisheden_US
dc.subjecttext classificationen_US
dc.subjectconvolutional neural networken_US
dc.subjectcross-language text classificationen_US
dc.subjectmultilingual classificationen_US
dc.subjecttransfer learningen_US
dc.subjectinductive transferen_US
dc.subjectsupervised learningen_US
dc.subjectartificial neural networken_US
dc.subjectarticleen_US
dc.titleCross-language text classification with convolutional neural networks from scratchen_US
dc.typeArticleen_US
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