Abstract
In current years, deep learning has showed promising results when used in the field of natural language processing (NLP). Neural Networks (NNs) such as convolutional neural network (CNN) and recurrent neural network (RNN) have been utilized for different NLP tasks like information retrieval, sentiment analysis and document classification. In this paper, we explore the use of NNs-based method for legal text classification. In our case, the results show that NN models with a fixed input length outperforms baseline methods.
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References
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
McCallum, A., Nigam, K., et al.: A comparison of event models for naive bayes text classification. In: AAAI-1998 Workshop on Learning for Text Categorization, vol. 752, pp. 41–48. Citeseer (1998)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of Tricks for Efficient Text Classification. CoRR, abs/1607.01759 (2016). http://arxiv.org/abs/1607.01759, arXiv:1607.01759. https://dblp.org/rec/bib/journals/corr/JoulinGBM16, dblp computer science bibliography, https://dblp.org. Accessed 13 Aug 2018
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)
Yogatama, D., Dyer, C., Ling, W., Blunsom, P.: Generative and discriminative text classification with recurrent neural networks. arXiv preprint arXiv:1703.01898 (2017)
Xiao, Y., Cho, K.: Efficient Character-Level Document Classification by Combining Convolution and Recurrent Layers, CoRR, abs/1602.00367 (2016). http://arxiv.org/abs/1602.00367, arXiv:1602.00367, https://dblp.org/rec/bib/journals/corr/XiaoC16, dblp computer science bibliography, https://dblp.org. Accessed 13 Aug 2018
Kim, Y.: Convolutional Neural Networks for Sentence Classification, CoRR, abs/1408.5882 (2014). http://arxiv.org/abs/1408.5882, arXiv:1408.5882, https://dblp.org/rec/bib/journals/corr/Kim14f, dblp computer science bibliography, https://dblp.org. Accessed 13 Aug 2018
Zhang, X., Zhao, J.J., LeCun, Y.: Character-Level Convolutional Networks for Text Classification, CoRR, abs/1509.01626 (2015). http://arxiv.org/abs/1509.01626, arXiv:1509.01626, https://dblp.org/rec/bib/journals/corr/ZhangZL15, dblp computer science bibliography, https://dblp.org. Accessed 13 Aug 2018
Conneau, A., Schwenk, H., Barrault, L., LeCun, Y.: Very Deep Convolutional Networks for Natural Language Processing, CoRR, abs/1606.01781 (2016). http://arxiv.org/abs/1606.01781, arXiv:1606.01781, https://dblp.org/rec/bib/journals/corr/ConneauSBL16, dblp computer science bibliography, https://dblp.org. Accessed 13 Aug 2018
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, Helsinki, Finland, vol. 8, pp. 160–167. ACM, New York (2008). http://doi.acm.org/10.1145/1390156.1390177, https://doi.org/10.1145/1390156.1390177.1390177. ISBN: 978–1-60558-205-4
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
MKalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. arXiv preprint arXiv:1404.2188 (2014)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems, pp. 649–657 (2015)
Koomsubha, T., Vateekul, P.: A character-level convolutional neural network with dynamic input length for Thai text categorization. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), pp. 101–105. IEEE (2017)
Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: Thirtieth AAAI Conference on Artificial Intelligence (2016)
Radford, A., Jozefowicz, R., Sutskever, I.: Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444 (2017)
Socher, R., et al.: 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 (2013)
Nallapati, R., Manning, C.D.: Legal docket-entry classification: Where machine learning stumbles. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 438–446. Association for Computational Linguistics (2008)
Sulea, O.-M., Zampieri, M., Malmasi, S., Vela, M., Dinu, L.P., van Genabith, J.: Exploring the use of text classification in the legal domain. arXiv preprint arXiv:1710.09306 (2017)
Undavia, S., Meyers, A., Ortega, J.E.: A Comparative study of classifying legal documents with neural networks. In: 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 515–522. IEEE (2018)
Da Silva, N.C., et al.: Document type classification for Brazil’s supreme court using a convolutional neural network. In: The Tenth International Conference on Forensic Computer Science and Cyber Law-ICoFCS, pp. 7–11 (2018)
Wei, F., Qin, H., Ye, S., Zhao, H.: Empirical study of deep learning for text classification in legal document review. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 3317–3320. IEEE (2018)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Wang, X., Liu, Y., Chengjie, S.U.N., Wang, B., Wang, X.: Predicting polarities of tweets by composing word embeddings with long short-term memory. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 1343–1353 (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014). JMLR.org
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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Hammami, E., Akermi, I., Faiz, R., Boughanem, M. (2019). Deep Learning for French Legal Data Categorization. In: Schewe, KD., Singh, N. (eds) Model and Data Engineering. MEDI 2019. Lecture Notes in Computer Science(), vol 11815. Springer, Cham. https://doi.org/10.1007/978-3-030-32065-2_7
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DOI: https://doi.org/10.1007/978-3-030-32065-2_7
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