Trailblazing AI research by Regology in collaboration with the University of Illinois at Urbana-Champaign – a year-long effort in conjunction with UIUC has been accepted to KDD’s esteemed Natural Legal Language Process (NLLP) 2020 Workshop.
Dominic Seyler, Paul Bruin, Pavan Bayyapu, Cheng Xiang Zhai
Terms in the laws of a legislature can be highly contextual: especially for corpora of codified laws and regulations where the reader has to be aware of the correct context when the corpus lacks a single level of hierarchy. The goal of this work is to assist professionals when reading legal text within a codified corpus by finding contextually consistent information units. To achieve this, we combine NLP and data mining techniques to develop a novel methodology that can find these information units in an unsupervised manner. Our method draws on expert experience and is modelled to emulate the “contextualization process” of experienced readers of legal content. We experimentally evaluate our method by comparing it to multiple expert-annotated datasets and find that our method achieves near-perfect performance on four state corpora and high precision on one federal corpus.
Full text of the paper at http://ceur-ws.org/Vol-2645/short1.pdf
Natural Legal Language Processing (NLLP) aims 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.
More information on NLLP can be found here
More information on 26TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING can be found here