Judgment2vec: Apply Graph Analytics to Searching and Recommendation of Similar Judgments

TL;DR

This paper introduces Judgment2vec, a graph-based approach combining expert-verified features and knowledge graph analysis to improve the efficiency of legal judgment similarity search and recommendation.

Contribution

It presents a novel method that integrates human-verified judgment features with graph analytics to automate and enhance legal case similarity detection.

Findings

Knowledge graph improves judgment similarity ranking

Combining expert scores with graph-based scores enhances accuracy

Reduces labor hours in legal case searches

Abstract

In court practice, legal professionals rely on their training to provide opinions that resolve cases, one of the most crucial aspects being the ability to identify similar judgments from previous courts efficiently. However, finding a similar case is challenging and often depends on experience, legal domain knowledge, and extensive labor hours, making veteran lawyers or judges indispensable. This research aims to automate the analysis of judgment text similarity. We utilized a judgment dataset labeled as the "golden standard" by experts, which includes human-verified features that can be converted into an "expert similarity score." We then constructed a knowledge graph based on "case-article" relationships, ranking each case using natural language processing to derive a "Node2vec similarity score." By evaluating these two similarity scores, we identified their discrepancies and relationships. The results can significantly reduce the labor hours required for legal searches and recommendations, with potential applications extending to various fields of information retrieval.