Court Judgement Labeling Using Topic Modeling and Syntactic Parsing

TL;DR

This paper presents a heuristic system that combines topic modeling, syntactic parsing, and legal term analysis to automatically label court judgements, aiding legal research and document retrieval.

Contribution

It introduces a novel approach integrating aspect-driven topic modeling with syntactic parsing and a legal term tree for effective judgement labeling and similar document recommendation.

Findings

System outperforms simple term extraction in summarization.

Recommendation algorithm is more effective than full-text comparison.

Approach shows potential for broader legal and other domain applications.

Abstract

In regions that practice common law, relevant historical cases are essential references for sentencing. To help legal practitioners find previous judgement easier, this paper aims to label each court judgement by some tags. These tags are legally important to summarize the judgement and can guide the user to similar judgements. We introduce a heuristic system to solve the problem, which starts from Aspect-driven Topic Modeling and uses Dependency Parsing and Constituency Parsing for phrase generation. We also construct a legal term tree for Hong Kong and implemented a sentence simplification module to support the system. Finally, we propose a similar document recommendation algorithm based on the generated tags. It enables users to find similar documents based on a few selected aspects rather than the whole passage. Experiment results show that this system is the best approach for this specific task. It is better than simple term extraction method in terms of summarizing the document, and the recommendation algorithm is more effective than full-text comparison approaches. We believe that the system has huge potential in law as well as in other areas.