Dynamic event type recognition and tagging for data-driven insights in law-enforcement

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.