In this article, we present a description of our systems as a part of our participation in the shared task namely Artificial Intelligence for Legal Assistance (AILA 2019). This is an integral event of Forum for Information Retrieval Evaluation-2019. The outcomes of this track would be helpful for the automation of the working process of the Indian Judiciary System. The manual working procedures and documentation at any level (from lower to higher court) of the judiciary system are very complex in nature. The systems produced as a part of this track would assist the law practitioners. It would be helpful for common men too. This kind of track also opens the path of research of Natural Language Processing (NLP) in the judicial domain. This track defined two problems such as Task 1: Identifying relevant prior cases for a given situation and Task 2: Identifying the most relevant statutes for a given situation. We tackled both of them. Our proposed approaches are based on BM25 and Doc2Vec. As per the results declared by the task organizers, we are in 3rd and a modest position in Task 1 and Task 2 respectively.
翻译:在本条中,我们介绍了作为我们参与共同任务,即法律援助人工情报(ALIA 2019)的一部分的系统情况,这是信息检索评价论坛(ALIA 2019)的一项整体活动,该轨道的成果将有助于印度司法系统工作进程自动化,司法系统各级(从下至上)的人工工作程序和文件具有非常复杂的性质,作为这一轨道的一部分产生的系统将有助于法律从业人员,对普通人也有帮助。这种轨道也为司法领域的自然语言处理研究开辟了道路。这种轨道界定了两个问题,例如任务1:为特定情况确定以前的相关案件,任务2:为特定情况确定最相关的法规。我们处理了这两个问题。我们提出的办法以BM25和Doc2Vec为基础。根据任务组织者宣布的结果,我们在任务1和任务2中分别处于第三位和一个不大的位置。