Machine-Crowd Annotation Workflow for Event Understanding Across Collections and Domains

People need context to process the massive information online. Context is often expressed by a specific event taking place. The multitude of data streams used to mention events provide an inconceivable amount of information redundancy and perspectives. This poses challenges to both humans, i.e., to reduce the information overload and consume the meaningful information and machines, i.e., to generate a concise overview of the events. For machines to generate such overviews, they need to be taught to understand events. The goal of this research project is to investigate whether combining machines output with crowd perspectives boosts the event understanding of state-of-the-art natural language processing tools and improve their event detection. To answer this question, we propose an end-to-end research methodology for: machine processing, defining experimental data and setup, gathering event semantics and results evaluation. We present preliminary results that indicate crowdsourcing as a reliable approach for (1) linking events and their related entities in cultural heritage collections and (2) identifying salient event features (i.e., relevant mentions and sentiments) for online data. We provide an evaluation plan for the overall research methodology of crowdsourcing event semantics across modalities and domains.