Identifying Legal Party Members from Legal Opinion Texts Using Natural Language Processing

EasyChair Preprint 5458

Abstract

Law and order is a field that can highly benefit from the contribution of Natural Language Processing (NLP) to its betterment. An area in which NLP can be of immense help is for information retrieval from legal documents which function as legal databases. The extraction of legal parties from the aforementioned legal documents can be identified as a task of high importance since it has a significant impact on the proceeding contemporary legal cases. This study proposes a novel deep learning methodology that can be effectively used to find a solution to the problem of identifying legal party members in legal documents. In addition to that, in this paper, we introduce a novel dataset which was created by an expert in the legal domain. Evaluations for the solution presented in the paper show that our system has 90.89% precision and 91.69% recall for an unseen paragraph from a legal document, thus conforming the success of our attempt.

Keyphrases: Legal party identification, NER, Recurrent Neural Networks, co-reference resolution

@booklet{EasyChair:5458,
  author    = {Chamodi Samarawickrama and Melonie de Almeida and Amal Shehan Perera and Nisansa de Silva and Gathika Ratnayaka},
  title     = {Identifying Legal Party Members from Legal Opinion Texts Using Natural Language Processing},
  howpublished = {EasyChair Preprint 5458},
  year      = {EasyChair, 2021}}