Abstract
Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language processing. Additionally, we share pre-trained legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others.
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Notes
You may find a large collection of such pre-trained models at https://github.com/3Top/word2vec-api.
References
Bojanowski P, Grave E, Joulin A, Mikolov T (2016) Enriching word vectors with subword information. arXiv preprint arXiv:160704606
Branting LK, Yeh A, Weiss B, Merkhofer E, Brown B (2017) Inducing predictive models for decision support in administrative adjudication. In: Proceedings of the MIREL Workshop on the The 16th International Conference on Artificial Intelligence and Law, London, UK
Chalkidis I, Androutsopoulos I (2017) A deep learning approach to contract element extraction. In: Proceedings of the 30th International Conference on Legal Knowledge and Information Systems, Luxembourg, pp 155–164
Chalkidis I, Androutsopoulos I, Michos A (2017) Extracting contract elements. In: Proceedings of The 16th International Conference on Artificial Intelligence and Law, London, UK, pp 19–28
Chalkidis I, Androutsopoulos I, Michos A (2018) Obligation and prohibition extraction using hierarchical rnns. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia
Do PK, Nguyen HT, Tran CX, Nguyen MT, Nguyen ML (2017) Legal Question Answering using Ranking SVM and Deep Convolutional Neural Network. CoRR abs/1703.0. arXiv:1703.05320
Firth JR (1935) The technique of semantics. Trans Philos Soc 34(1):36–73
Goldberg Y (2017) Neural network methods in natural language processing. Morgan and Claypool Publishers, San Rafael
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, pp 328–339
Kim My, Xu Y, Goebel R (2015) A convolutional neural network in legal question answering. In: Ninth International Workshop on Juris-informatics (JURISIN)
Kudo T, Yamamoto K, Matsumoto Y (2004) Applying conditional random fields to Japanese morphological analysis. In: Proceedings of the Empirical Methods for Natural Language Processing, Barcelona, Spain, pp 230–237
Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, Stateline, NV
Morimoto A, Kubo D, Sato M, Shindo H, Matsumoto Y (2017) Legal question answering system using neural attention. In: Proceedings of COLIEE - International Conference on Artificial Intelligence and Law, London, UK
Nanda R, John AK, Caro LD, Boella G, Robaldo L (2017) Legal information retrieval using topic clustering and neural networks. In: 4th competition on legal information extraction and entailment, 16th international conference on artificial intelligence and law vol. 47, pp 68–78
Nejadgholi I, Bougueng R, Witherspoon S (2017) A semi-supervised training method for semantic search of legal facts in Canadian immigration cases. Front Artif Intell Appl 302:125–134
Nguyen T, Nguyen L, Tojo S, Satoh K, Shimazu A (2017) Single and multiple layer BI-LSTM-CRF for recognizing requisite and effectuation parts in legal texts. In: 2nd ASAIL Workshop on 16th 16th international conference on artificial intelligence and law, London, UK
Nguyen T, Nguyen L, Tojo S, Satoh K, Shimazu A (2018) Recurrent neural network-based models for recognizing requisite and effectuation parts in legal texts. Artif Intell Law 26(2):169–199
O’Neill J, Buitelaar P, Robin C, Brien LO (2017) Classifying Sentential Modality in Legal Language: A Use Case in Financial Regulations, Acts and Directives. In: Proceedings of the 16th international conference on artificial intelligence and law, London, UK, pp 159–168
Pennington J, Socher R, Manning CD (2014) GloVe: Global vectors for word representation. In: Proceedings of the conference on empirical methods in natural language processing, Doha, Qatar, pp 1532–1543
Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Conference of NA chapter of the association for computational linguistics, New Orleans, Louisiana, USA
Tang G, Guo H, Guo Z, Xu S (2016) Matching law cases and reference law provision with a neural attention model. IBM China Research, Beijing
Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 15th conference of the North American chapter of the association for computational linguistics: human language technologies, San Diego, CA, USA, pp 1480–1489
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Chalkidis, I., Kampas, D. Deep learning in law: early adaptation and legal word embeddings trained on large corpora. Artif Intell Law 27, 171–198 (2019). https://doi.org/10.1007/s10506-018-9238-9
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DOI: https://doi.org/10.1007/s10506-018-9238-9