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

• DOI: 10.1007/s10506-018-9238-9
• Publication Date: 2018-12-10T20:31:33Z
• Authors: Ilias Chalkidis; Dimitrios Kampas
• Keywords: Word2vec; Computational linguistics; Legal case; Corpus Linguistics; Feature Engineering

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.