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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/1311.2524 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119. Curran Associates Inc. (2013)
Kim, Y.: Convolutional neural networks for sentence classification. CoRR, abs/1408.5882 (2014)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. CoRR, abs/1404.2188 (2014)
dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, Dublin, Ireland, pp. 69–78. Dublin City University and Association for Computational Linguistics, August 2014
Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. CoRR, abs/1612.08083 (2016)
Zhang, X., Zhao, J.J., LeCun, Y.: Character-level convolutional networks for text classification. CoRR, abs/1509.01626 (2015)
Vosoughi, S., Vijayaraghavan, P., Roy, D.: Tweet2vec: learning tweet embeddings using character-level CNN-LSTM encoder-decoder. CoRR, abs/1607.07514 (2016)
Cook, D.J., Feuz, K.D., Krishnan, N.C.: Transfer learning for activity recognition: a survey. Knowl. Inf. Syst. 36, 537–556 (2013)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.-F.: Imagenet large scale visual recognition challenge. CoRR, abs/1409.0575 (2014)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359, October 2010
Howard, J., Ruder, S.: Fine-tuned language models for text classification. ArXiv e-prints, January 2018
Agrawal, P., Girshick, R.B., Malik, J.: Analyzing the performance of multilayer neural networks for object recognition. CoRR, abs/1407.1610 (2014)
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: an astounding baseline for recognition. CoRR, abs/1403.6382 (2014)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? CoRR, abs/1411.1792 (2014)
Mou, L., Meng, Z., Yan, R., Li, G., Xu, Y., Zhang, L., Jin, Z.: How transferable are neural networks in NLP applications? CoRR, abs/1603.06111 (2016)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. CoRR, abs/cs/0506075 (2005)
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631–1642, Seattle, Washington, USA. Association for Computational Linguistics, October 2013
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-94649-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94648-1
Online ISBN: 978-3-319-94649-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)