In law enforcement, investigators are typically tasked with analyzing large collections of evidences in order to identify and extract key information to support investigation cases. In this context, events are key elements that help understanding and reconstructing what happened from the collection of evidence items. With the ever increasing amount of data (e.g., e-mails and content from social media) gathered today as part of investigation tasks (in most part done manually), managing such amount of data can be challenging and prone to missing important details that could be of high relevance to a case. In this paper, we aim to facilitate the work of investigators through a framework for deriving insights from data. We focus on the auto-recognition and dynamic tagging of event types (e.g., phone calls) from (textual) evidence items, and propose a framework to facilitate these tasks and provide support for insights and discovery. The experimental results obtained by applying our approach to a real, legal dataset demonstrate the feasibility of our proposal by achieving good performance in the task of automatically recognizing and tagging event types of interest.

中文翻译：

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动态事件类型识别和标记，用于执法中的数据驱动洞察

在执法中，调查人员的任务通常是分析大量证据，以识别和提取关键信息以支持调查案件。在这种情况下，事件是帮助理解和重建从收集的证据项目中发生的事情的关键元素。随着今天作为调查任务的一部分（大部分是手动完成的）收集的数据量（例如，来自社交媒体的电子邮件和内容）不断增加，管理如此大量的数据可能具有挑战性，并且容易遗漏重要的细节，这些细节可能与案件高度相关。在本文中，我们旨在通过一个从数据中获取洞察力的框架来促进调查人员的工作。我们专注于来自（文本）证据项目的事件类型（例如，电话）的自动识别和动态标记，并提出一个框架来促进这些任务并为洞察力和发现提供支持。通过将我们的方法应用于真实的合法数据集而获得的实验结果证明了我们的建议的可行性，因为在自动识别和标记感兴趣的事件类型的任务中取得了良好的性能。