Deep Learning for Named-Entity Linking with Transfer Learning for Legal Documents

Ahmed Elnaggar, Robin Otto, F. Matthes
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引用次数: 10

Abstract

In the legal domain it is important to differentiate between words in general, and afterwards to link the occurrences of the same entities. The topic to solve these challenges is called Named-Entity Linking (NEL). Current supervised neural networks designed for NEL use publicly available datasets for training and testing. However, this paper focuses especially on the aspect of applying transfer learning approach using networks trained for NEL to legal documents. Experiments show consistent improvement in the legal datasets that were created from the European Union law in the scope of this research. Using transfer learning approach, we reached F1-score of 98.90% and 98.01% on the legal small and large test dataset.
命名实体链接的深度学习与法律文件的迁移学习
在法律领域,重要的是要区分一般的单词,然后将相同实体的出现联系起来。解决这些挑战的主题被称为命名实体链接(NEL)。目前为NEL设计的监督神经网络使用公开可用的数据集进行训练和测试。然而,本文特别关注将迁移学习方法应用于法律文件的方面,使用经过NEL训练的网络。实验表明,在本研究范围内,从欧盟法律创建的法律数据集得到了持续的改进。使用迁移学习方法,我们在合法的小型和大型测试数据集上分别达到了98.90%和98.01%的f1得分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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