Legal Document Retrieval using Document Vector Embeddings and Deep Learning

TL;DR

This paper introduces three novel vector space models for legal document retrieval using deep learning and semantic measures, demonstrating improved accuracy over standard methods.

Contribution

Developed and compared three innovative models for legal document retrieval that incorporate semantic similarity and ensemble techniques, reducing reliance on domain experts.

Findings

Ensemble model achieves higher accuracy in legal document retrieval.

Semantic word measures improve vector representations.

Varying document vector dimensions impacts retrieval performance.

Abstract

Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. Many researchers have incorporated different techniques to overcome the technical and domain specificity and provide a mature model for various domains of interest. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain. The three different models incorporated vector space representations of the legal domain, where document vector generation was done in two different mechanisms and as an ensemble of the above two. This study contains the research being carried out in the process of representing legal case documents into different vector spaces, whilst incorporating semantic word measures and natural language processing techniques. The ensemble model built in this study, shows a significantly higher accuracy level, which indeed proves the need for incorporation of domain specific semantic similarity measures into the information retrieval process. This study also shows, the impact of varying distribution of the word similarity measures, against varying document vector dimensions, which can lead to improvements in the process of legal information retrieval.