Who? What? Event tracking needs event understanding

Abstract

Humans first learn, then think and finally perform a task. Machines neither learn nor think, but we still expect them to perform tasks as well as humans. In this position paper, we address the lack of understanding in Topic Detection and Tracking (TDT), an area that builds timelines of events, but which hardly understands events at all. Without understanding events, TDT has progressed slowly as the community struggles to solve the challenges of modern data sources, like Twitter. We explore understanding from different perspectives: what it means for machines to understand events, why TDT needs understanding, and how algorithms can generate knowledge automatically. To generate understanding, we settle on a structured definition of events based on the four Ws: the Who, What, Where and When. Of the four Ws, we focus especially on the Who and the What, aligning them with other research areas that can help TDT generate event knowledge automatically. In time, understanding can lead to machines that not only track events better, but also model and mine them.