Natural Language Processing and Machine Learning for Law and Policy Texts

35 Pages Posted: 18 Dec 2019

See all articles by John Nay

John Nay

New York University (NYU); Skopos Labs, Inc.

Date Written: April 7, 2018

Abstract

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.

Keywords: Machine Learning, Artificial Intelligence, Prediction, Policy, Legal Informatics, AI & Law, Congress, Presidency, Political Science, Empirical Legal Studies

Suggested Citation

Nay, John, Natural Language Processing and Machine Learning for Law and Policy Texts (April 7, 2018). Available at SSRN: https://ssrn.com/abstract=3438276 or http://dx.doi.org/10.2139/ssrn.3438276

John Nay (Contact Author)

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

Skopos Labs, Inc.

NY
United States

HOME PAGE: http://skoposlabs.com

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