arXiv 2102.048452021-02-09cs.IR

CNN在法律文书审读中的应用

CNN Application in Detection of Privileged Documents in Legal Document Review

Rishi Chhatwal, Robert Keeling, Peter Gronvall, Nathaniel Huber-Fliflet, Jianping Zhang, Haozhen Zhao

备注：: 2020 IEEE International Conference on Big Data (Big Data)

摘要：

保护特权通信和数据不被披露对法律团队至关重要。法律建议，如律师与客户的沟通或诉讼策略，通常免于在诉讼或监管事件中披露，对律师与客户的关系至关重要。为了保护这些信息不被披露，公司和外部法律顾问经常审查大量文件，以确定哪些文件包含特权材料。这个过程是非常昂贵和耗时的。随着数据量的增加，法律顾问通常采用各种方法减少需要审查的文件数量，同时平衡确保保护特权信息的需要。关键词搜索是一种针对特权信息和减少文档审查人群的方法。关键字搜索在广撒网方面是有效的，但通常返回的结果过于包容-其中大多数不包含特权信息。为了克服关键字搜索的弱点，法律团队越来越多地使用机器学习技术来定位特权信息。在这些研究中，经典的文本分类技术被应用于建立分类模型，以识别特权文档。在本文中，作者提出了一种不同的方法，通过应用机器学习/卷积神经网络技术（CNN）来识别特权文档。我们提出的方法结合了关键字搜索与CNN。对于每个关键词术语，使用关键词出现的上下文创建CNN模型。此外，提出了一种方法来选择可靠的特权（积极）的训练关键字出现从标记的积极的训练文件。大量的实验结果表明，所提出的方法可以显着减少误报，同时仍然捕获大部分的真阳性。

英文摘要：

Protecting privileged communications and data from disclosure is paramount for legal teams. Legal advice, such as attorney-client communications or litigation strategy are typically exempt from disclosure in litigations or regulatory events and are vital to the attorney-client relationship. To protect this information from disclosure, companies and outside counsel often review vast amounts of documents to determine those that contain privileged material. This process is extremely costly and time consuming. As data volumes increase, legal counsel normally employs methods to reduce the number of documents requiring review while balancing the need to ensure the protection of privileged information. Keyword searching is relied upon as a method to target privileged information and reduce document review populations. Keyword searches are effective at casting a wide net but often return overly inclusive results - most of which do not contain privileged information. To overcome the weaknesses of keyword searching, legal teams increasingly are using machine learning techniques to target privileged information. In these studies, classic text classification techniques are applied to build classification models to identify privileged documents. In this paper, the authors propose a different method by applying machine learning / convolutional neural network techniques (CNN) to identify privileged documents. Our proposed method combines keyword searching with CNN. For each keyword term, a CNN model is created using the context of the occurrences of the keyword. In addition, a method was proposed to select reliable privileged (positive) training keyword occurrences from labeled positive training documents. Extensive experiments were conducted, and the results show that the proposed methods can significantly reduce false positives while still capturing most of the true positives.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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