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.

本文旨在解决 AILA2021（法律协助人工智能）中的任务之一，在使用 BERT 获取的句子嵌入和线性分类器的结合下，将文档在语义上分割为 7 个预定义标签或 “修辞角色”，通过对实验加权分析，表明赋予最高频率类别更大的权重可获得更好的结果。在任务 1 中，团队 legalNLP 获得了 0.22 的 F1 分数。