Autosuggestion of Relevant Cases and Statutes

EasyChair Preprint 9137

Abstract

A precedent or statute that has been cited frequently to solve many similar or identical legal issues is considered to be highly relevant when a similar issue arises. With this paper, we aim to create an auto suggestion tool to predict the most relevant cases and statutes for the specified legal issue/query. Our approach considers the cited cases and statutes as single tokens having unique IDs as the value where we try to find the relevant tokens based on the words describing the legal issues around these tokens. We observed that context-based representations outperformed lexical-based representations and distributional representations. Moreover, we observed that the method works better for statute law retrieval compared to case law retrieval.

Keyphrases: Citation Recommendation, Context Based Representation, Distributional representation, Masked Language Model, Predecents, statutes

@booklet{EasyChair:9137,
  author    = {Saran Pandian and Shubham Joshi},
  title     = {Autosuggestion of Relevant Cases and Statutes},
  howpublished = {EasyChair Preprint 9137},
  year      = {EasyChair, 2022}}