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We present a novel approach to automatically annotating broadcast video. To manage the enormous variety of objects, events and scenes in video problem domains such as news video, we couple generic image analysis with a semantic database, WordNet, containing huge amounts of real-world information. Object and event recognition are performed by searching WordNet for concepts jointly supported by image evidence and topic context derived from the video transcript. No object- specific or event-specific training is required, and only a few object models and detection algorithms are required to label much of the significant content of news video. The hierarchical structure of WordNet yields hierarchical recognition, dynamically tailored to the level of supporting image evidence. The potential of the approach is demonstrated by analyzing a wide variety of scenes in news video.