Legal Search in Case Law and Statute Law

08/23/2021
by   Julien Rossi, et al.
0

In this work we describe a method to identify document pairwise relevance in the context of a typical legal document collection: limited resources, long queries and long documents. We review the usage of generalized language models, including supervised and unsupervised learning. We observe how our method, while using text summaries, overperforms existing baselines based on full text, and motivate potential improvement directions for future work.

READ FULL TEXT
research
03/16/2023

A Short Survey of Viewing Large Language Models in Legal Aspect

Large language models (LLMs) have transformed many fields, including nat...
research
10/14/2022

Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation

Summarization of legal case judgement documents is a challenging problem...
research
12/14/2019

Long-length Legal Document Classification

One of the principal tasks of machine learning with major applications i...
research
09/04/2022

ArgLegalSumm: Improving Abstractive Summarization of Legal Documents with Argument Mining

A challenging task when generating summaries of legal documents is the a...
research
04/20/2021

StateCensusLaws.org: A Web Application for Consuming and Annotating Legal Discourse Learning

In this work, we create a web application to highlight the output of NLP...
research
11/22/2019

Use of Artificial Intelligence to Analyse Risk in Legal Documents for a Better Decision Support

Assessing risk for voluminous legal documents such as request for propos...
research
10/12/2015

Towards Meaningful Maps of Polish Case Law

In this work, we analyze the utility of two dimensional document maps fo...

Please sign up or login with your details

Forgot password? Click here to reset