Basic elements of the law, such as statuses and regulations, are embodied in natural language, and strictly depend on linguistic expressions. Hence, analyzing legal contents is a challenging task, and the legal domain is increasingly looking for automatic-processing support. This paper focuses on a specific context in the legal domain, which has so far remained unexplored: automatic processing of garnishment documents. A garnishment is a legal procedure by which a creditor can collect what a debtor owes by requiring to confiscate a debtor’s property (e.g., a checking account) that is hold by a third party, dubbed garnishee. Our proposal, motivated by a real-world use case, is a versatile natural-language-processing pipeline to support a garnishee in the processing of a large-scale flow of garnishment documents. In particular, we mainly focus on two tasks: (i) categorize received garnishment notices onto a predefined taxonomy of categories; (ii) perform an information-extraction phase, which consists in automatically identifying from the text various information, such as identity of involved actors, amounts, and dates. The main contribution of this work is to describe challenges, design, implementation, and performance of the core modules and methods behind our solution. Our proposal is a noteworthy example of how data-science techniques can be successfully applied to a novel yet challenging real-world context.

法律的基本要素（例如状态和法规）以自然语言体现，并且严格依赖语言表达。因此，分析法律内容是一项具有挑战性的任务，并且法律领域越来越多地寻求自动处理支持。本文关注的是法律领域中的特定上下文，迄今为止尚未探索：装饰的自动处理文件。扣押是一种法律程序，债权人可以通过该程序要求没收由第三方（被称为被扣押人）持有的债务人的财产（例如，支票账户）来收集债务人的欠款。我们的建议受现实用例的启发，是一种通用的自然语言处理管道，可支持被收件者处理大规模的装饰文件流。特别是，我们主要集中在两个任务上：（i）将收到的装饰通知书分类到预定义的类别中；（ii）执行信息提取阶段，该阶段包括从文本中自动识别各种信息，例如所涉及的参与者的身份，金额和日期。这项工作的主要贡献是描述挑战，设计，实施，解决方案背后的核心模块和方法的性能。我们的建议是一个值得注意的例子，说明了如何将数据科学技术成功应用于新颖而又充满挑战的现实环境。