Court Judgement Labeling Using Topic Modeling and Syntactic Parsing
This work addresses the need for efficient case retrieval in common law regions, offering a domain-specific tool for legal practitioners, though it is incremental as it builds on existing NLP techniques.
The paper tackled the problem of labeling court judgements with tags to help legal practitioners find relevant historical cases, introducing a system that combines topic modeling and syntactic parsing to generate tags and recommend similar documents, achieving better performance than simple term extraction and full-text comparison methods.
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.