HKLII has served as the repository of legal documents in Hong Kong for a decade. Our team aims to incorporate NLP techniques into the website to make it more intelligent. To achieve this goal, this individual task is 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.
翻译:香港法律文件库已经作为香港法律文件存放了十年。 我们的团队旨在将NLP技术纳入网站,使其更聪明。 为了实现这一目标, 个人的任务就是用一些标签给每个法院的判决贴上标签。 这些标签对于总结判决具有法律重要性, 可以引导用户找到类似的判决。 我们引入了一种从Aspect驱动的专题建模开始解决问题的繁忙系统, 并使用依赖性分类和属性分析法来生成短语。 我们还为香港建造了一条法律术语树, 并实施了句式简化模块来支持系统。 最后, 我们根据生成的标签提出了类似的文件建议算法。 它使用户能够根据几个选定的方面而不是整个段落找到类似的文件。 实验结果显示, 这个系统是完成这一具体任务的最佳方法。 在总结文件方面,比简单的术语抽取法更好,建议算法比全文比较法更有效。 我们相信, 该系统在法律和其他领域都具有巨大的潜力。