2nd Workshop on Natural Legal Language Processing (NLLP)

Goal

We aim to bring together researchers and practitioners from Data Science (DS), Natural Language Processing (NLP), Machine Learning (ML) and other Artificial Intelligence (AI) disciplines, and the legal domain.

The NLLP workshop was held for the first time in 2019, collocated with the Conference of the North-American Association of Computational Linguistics (NAACL). The first edition consisted of 12 original papers, 9 of which are archived in the workshop proceedings.

Building on the success of the first edition, the 2nd edition of the NLLP workshop will be collocated with KDD 2020.

Motivation

As electronic information becomes increasingly available around the world, automated tools for processing that information have grown apace. These tools can be especially effective and time-saving on text where information can be distilled in interesting ways including auto-summarization, named-entity extraction, machine translation, sentiment analysis, topic classification and others. As a result, natural language processing (NLP) applications are popular in important commercial contexts such as finance and healthcare.

The Legal domain however is still largely underrepresented in the NLP literature despite its enormous potential for generating interesting research problems on a par with other important commercial areas. In fact the US Legal Services market alone is valued at 211 billion according to US government price indices.

The accessibility of legal texts in the US in particular was an issue in the past preventing some researchers from working on legal NLP problems. Over the last few years however, more legal corpora have come online at low- or no-cost including the BYU Corpus, the Free Law Project and the expansion of resources published by the Library of Congress through Law.gov. A variety of growing electronic legal resources already exist free of charge for countries in Europe and Asia. Thus we feel that the timing is excellent to bring together researchers from around the world to focus on NLP problems in this area.

We consider “legal text” to include litigation-related corpora such as dockets, opinions and court transcripts but also corpora based on patents, briefs, public financial filings, civil code, local ordinances, privacy policies, law enforcement records, congressional records and speeches.