Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective
Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective
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DOI：
10.7717/peerj-cs.93
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发表时间：
2016-10-01
影响因子：
3.8
通讯作者：
Lampos, Vasileios
中科院分区：
文献类型：
--
作者：
Aletras, Nikolaos;Tsarapatsanis, Dimitrios;Lampos, Vasileios
自然语言处理和机器学习的最新进展为我们提供了构建预测模型的工具，这些模型可用于揭示驱动司法决策的模式。对于律师和法官来说，这是一种有用的辅助工具，可以迅速查明案件并提取导致某些决定的模式。本文首次系统地研究了仅根据文本内容来预测欧洲人权法院审理案件的结果。我们制定了一个二元分类任务，其中分类器的输入是从案例中提取的文本内容，目标输出是对是否存在违反人权公约条款的实际判断。文本信息使用连续的词序列表示，即n -gram和主题。我们的模型预测法院判决的准确率很高（平均为79%）。实证分析表明，案件的形式事实是最重要的预测因素。这与法律现实主义理论认为司法决策受到事实刺激的显著影响是一致的。我们还观察到，一个案例的主题内容是这个分类任务的另一个重要特征，并通过进行定性分析进一步探讨这种关系。
Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court's decisions with a strong accuracy (79% on average). Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis.