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Artificial Intelligence and Law

Erschienen in:

11.12.2018

Deep learning in law: early adaptation and legal word embeddings trained on large corpora

verfasst von: Ilias Chalkidis, Dimitrios Kampas

Erschienen in: Artificial Intelligence and Law | Ausgabe 2/2019

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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.

Vorheriger Artikel Building a corpus of legal argumentation in Japanese judgement documents: towards structure-based summarisation

Nächster Artikel Unsupervised and supervised text similarity systems for automated identification of national implementing measures of European directives

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You may find a large collection of such pre-trained models at https://github.com/3Top/word2vec-api.

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Titel: Deep learning in law: early adaptation and legal word embeddings trained on large corpora
verfasst von: Ilias Chalkidis
Dimitrios Kampas
Publikationsdatum: 11.12.2018
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence and Law / Ausgabe 2/2019
Print ISSN: 0924-8463
Elektronische ISSN: 1572-8382
DOI: https://doi.org/10.1007/s10506-018-9238-9

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