多类分类法在法律案件中的重要句子识别(CS)

自然语言处理(NLP)的发展正以实际应用和学术兴趣的形式在各个领域传播。从本质上讲，法律领域包含大量文本格式的数据。因此，需要应用自然语言处理来满足该领域的分析需求。对于法律专业人士来说，在法律案件中识别重要的句子、事实和论据是一项非常乏味的工作。在本研究中，我们从案件中主要当事人的角度，探讨了句子嵌入的多类分类，以识别法律案件中的重要句子。此外，为了提高直接使用类别交叉熵损失的准确性，定义了任务特定的损失函数。

原文题目：Important Sentence Identification in Legal Cases Using Multi-Class Classification

原文：The advancement of Natural Language Processing (NLP) is spreading through various domains in forms of practical applications and academic interests. Inherently, the legal domain contains a vast amount of data in text format. Therefore it requires the application of NLP to cater to the analytically demanding needs of the domain. Identifying important sentences, facts and arguments in a legal case is such a tedious task for legal professionals. In this research we explore the usage of sentence embeddings for multi-class classification to identify important sentences in a legal case, in the perspective of the main parties present in the case. In addition, a task-specific loss function is defined in order to improve the accuracy restricted by the straightforward use of categorical cross entropy loss.