Detection As Multi-Topic Tracking

The topic tracking task from TDT is a variant of information filtering tasks that focuses on event-based topics in streams of broadcast news. In this study, we compare tracking to another TDT task, detection, which has the goal of partitioning all arriving news into topics, regardless of whether the topics are of interest to anyone, and even when a new topic appears that had not been previous anticipated. There are clear relationships between the two tasks (under some assumptions, a “perfect” tracking system could “solve” the detection problem), but they are evaluated quite differently. We describe the two tasks and discuss their similarities. We show how viewing detection as a form of multi-topic parallel tracking can illuminate the performance tradeoffs of detection over tracking.