Natural Language Processing (NLP) and Information Retrieval (IR) in the judicial domain is an essential task. With the advent of availability domain-specific data in electronic form and aid of different Artificial intelligence (AI) technologies, automated language processing becomes more comfortable, and hence it becomes feasible for researchers and developers to provide various automated tools to the legal community to reduce human burden. The Competition on Legal Information Extraction/Entailment (COLIEE-2019) run in association with the International Conference on Artificial Intelligence and Law (ICAIL)-2019 has come up with few challenging tasks. The shared defined four sub-tasks (i.e. Task1, Task2, Task3 and Task4), which will be able to provide few automated systems to the judicial system. The paper presents our working note on the experiments carried out as a part of our participation in all the sub-tasks defined in this shared task. We make use of different Information Retrieval(IR) and deep learning based approaches to tackle these problems. We obtain encouraging results in all these four sub-tasks.
翻译:司法领域的自然语言处理(NLP)和信息检索(IR)是一项基本任务,随着以电子形式提供的具体领域数据和不同人工智能(AI)技术的帮助的出现,自动语言处理变得更加舒适,因此研究人员和开发商可以向法律界提供各种自动化工具,以减轻人类负担。法律信息提取/销售竞争(COLIEE-2019)与人造情报和法律国际会议(ICAIL)合作,在2019年的人工智能和法律会议(ICAIL)下,产生了很少具有挑战性的任务。共有的四种界定的子任务(即任务1、任务2、任务3和任务4),这四个子任务将能够为司法系统提供少量自动化系统。本文介绍了我们作为我们参与这一共同任务中界定的所有子任务的一部分所进行的实验工作说明。我们利用不同的信息检索和深层次学习方法来解决这些问题。我们在所有这四个子任务中都取得了令人鼓舞的成果。