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Authors: Minato Sato ; Ryohei Orihara ; Yuichi Sei ; Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: The University of Electro-Communications, Japan

ISBN: 978-989-758-220-2

Keyword(s): Deep Learning, Temporal ConvNets, Transfer Learning, Text Classification, Sentiment Analysis.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Natural Language Processing ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Temporal (one-dimensional) Convolutional Neural Network (Temporal CNN, ConvNet) is an emergent technology for text understanding. The input for the ConvNets could be either a sequence of words or a sequence of characters. In the latter case there are no needs for natural language processing that depends on a language such as morphological analysis. Past studies showed that the character-level ConvNets worked well for news category classification and sentiment analysis / classification tasks in English and romanized Chinese text corpus. In this article we apply the character-level ConvNets to Japanese text understanding. We also attempt to reuse meaningful representations that are learned in the ConvNets from a large-scale dataset in the form of transfer learning, inspired by its success in the field of image recognition. As for the application to the news category classification and the sentiment analysis and classification tasks in Japanese text corpus, the ConvNets outperformed N-gr am-based classifiers. In addition, our ConvNets transfer learning frameworks worked well for a task which is similar to one used for pre-training. (More)

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Paper citation in several formats:
Sato, M.; Orihara, R.; Sei, Y.; Tahara, Y. and Ohsuga, A. (2017). Japanese Text Classification by Character-level Deep ConvNets and Transfer Learning.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 175-184. DOI: 10.5220/0006193401750184

@conference{icaart17,
author={Minato Sato. and Ryohei Orihara. and Yuichi Sei. and Yasuyuki Tahara. and Akihiko Ohsuga.},
title={Japanese Text Classification by Character-level Deep ConvNets and Transfer Learning},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={175-184},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006193401750184},
isbn={978-989-758-220-2},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Japanese Text Classification by Character-level Deep ConvNets and Transfer Learning
SN - 978-989-758-220-2
AU - Sato, M.
AU - Orihara, R.
AU - Sei, Y.
AU - Tahara, Y.
AU - Ohsuga, A.
PY - 2017
SP - 175
EP - 184
DO - 10.5220/0006193401750184

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