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

TL;DR

The paper presents elsarticle.cls, a dedicated LaTeX document class for Elsevier manuscript submissions that is built on article.cls to minimize conflicts with installed packages. It formalizes dependency on standard tools such as natbib, geometry, and hyperref, and supports configurable front matter including abstracts and keywords. It documents major differences from the predecessor elsart.cls, emphasizing reduced package clashes, flexible preprint and final-format options, and improved handling of long titles and front matter. The work provides installation procedures and usage guidance, enabling researchers to prepare well-formatted submissions with consistent margins, citations, and float management. Overall, elsarticle.cls aims to streamline submission workflows and reduce formatting friction for Elsevier journals.

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.