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2018 | OriginalPaper | Chapter

Neural Networks for Multi-lingual Multi-label Document Classification

Authors : Jiří Martínek, Ladislav Lenc, Pavel Král

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

This paper proposes a novel approach for multi-lingual multi-label document classification based on neural networks. We use popular convolutional neural networks for this task with three different configurations. The first one uses static word2vec embeddings that are let as is, while the second one initializes it with word2vec and fine-tunes the embeddings while learning on the available data. The last method initializes embeddings randomly and then they are optimized to the classification task. The proposed method is evaluated on four languages, namely English, German, Spanish and Italian from the Reuters corpus. Experimental results show that the proposed approach is efficient and the best obtained F-measure reaches 84%.

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Metadata
Title
Neural Networks for Multi-lingual Multi-label Document Classification
Authors
Jiří Martínek
Ladislav Lenc
Pavel Král
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01418-6_8

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