论文标题
嵌入句子的法律援助分类
Classification on Sentence Embeddings for Legal Assistance
论文作者
论文摘要
法律程序需要大量时间，而且花费很多。律师必须做很多工作，以确定先前案件和法规的不同部分。该论文试图解决将在Fire2021举行的AILA2021（人工智能以寻求法律援助）（信息检索评估论坛）。任务是将文档分割为不同的7个预定义标签或“修辞角色”中的不同的一个。该论文使用BERT从句子中获取句子嵌入，然后使用线性分类器来输出最终预测。该实验表明，当将更多的权重分配给具有最高频率的课程时，结果比在频率较低的班级给出更多权重时要好。在任务1中，LegalNLP团队的F1得分为0.22。
Legal proceedings take plenty of time and also cost a lot. The lawyers have to do a lot of work in order to identify the different sections of prior cases and statutes. The paper tries to solve the first tasks in AILA2021 (Artificial Intelligence for Legal Assistance) that will be held in FIRE2021 (Forum for Information Retrieval Evaluation). The task is to semantically segment the document into different assigned one of the 7 predefined labels or "rhetorical roles." The paper uses BERT to obtain the sentence embeddings from a sentence, and then a linear classifier is used to output the final prediction. The experiments show that when more weightage is assigned to the class with the highest frequency, the results are better than those when more weightage is given to the class with a lower frequency. In task 1, the team legalNLP obtained a F1 score of 0.22.
