From the abstract for John Nay, Natural Language Processing and Machine Learning for Law and Policy Texts (Aug. 23, 2019):

Almost all law is expressed in natural language; therefore, natural language processing (NLP) is a key component of understanding and predicting law at scale. NLP converts unstructured text into a formal representation that computers can understand and analyze. The intersection of NLP and law is poised for innovation because there are (i.) a growing number of repositories of digitized machine-readable legal text data, (ii.) advances in NLP methods driven by algorithmic and hardware improvements, and (iii.) the potential to improve the effectiveness of legal services due to inefficiencies in its current practice.

NLP is a large field and like many research areas related to computer science, it is rapidly evolving. Within NLP, this paper focuses primarily on statistical machine learning techniques because they demonstrate significant promise for advancing text informatics systems and will likely be relevant in the foreseeable future.

First, we provide a brief overview of the different types of legal texts and the different types of machine learning methods to process those texts. We introduce the core idea of representing words and documents as numbers. Then we describe NLP tools for leveraging legal text data to accomplish tasks. Along the way, we define important NLP terms in italics and offer examples to illustrate the utility of these tools. We describe methods for automatically summarizing content (sentiment analyses, text summaries, topic models, extracting attributes and relations, document relevance scoring), predicting outcomes, and answering questions.

“Natural language generation (NLG) is a subset of natural language processing (NLP) that aims to produce natural language from structured data,” wrote Sam Del Rowe. “It can be used in chatbot conversations, but also for various types of content creation, such as summarizing data and generating product descriptions for online shopping. Companies in the space offer various use cases for this type of automated content creation, but the technology requires human oversight—a necessity that is likely to remain in the near future.” For more, see Get Started With Natural Language Content Generation, EContent, July 22, 2019.