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Relaxation Method of Convolutional Neural Networks for Natural Language Processing

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Distributed Computing and Artificial Intelligence, 15th International Conference (DCAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 800))

Abstract

Deep learning has developed into one of the most powerful methods in the machine learning field. In particular, convolutional neural networks (CNNs) have been applied not only to image recognition tasks but also to natural language processing (NLP). To reuse older deep learning models, transfer learning techniques have been widely used in the image recognition field. However, there has been little research on transfer learning in NLP. In this paper, we propose a novel transfer learning model based on a relaxation method of CNNs for NLP. The effectiveness of the proposed method is verified using computer simulations, taking a film review score recognition task as an example.

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Notes

  1. 1.

    https://www.cs.cornell.edu/people/pabo/movie-review-data/.

  2. 2.

    http://nlp.stanford.edu/sentiment/.

  3. 3.

    https://code.google.com/p/word2vec/.

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Correspondence to Ryo Iwasaki .

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Iwasaki, R., Hasegawa, T., Mori, N., Matsumoto, K. (2019). Relaxation Method of Convolutional Neural Networks for Natural Language Processing. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_23

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