Daniel Morozoff-Abegauz

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News stories are rarely understood in isolation. Every story is driven by key entities that give the story its context. Persons, places, times, and several surrounding topics can often succinctly represent a news event, but are only useful if they can be both identified and linked together. We introduce a novel architecture called News Rover for re-bundling(More)
We present a fully automatic system from raw data gathering to navigation over heterogeneous news sources, including over 18k hours of broadcast video news, 3.58M online articles, and 430M public Twitter messages. Our system addresses the challenge of extracting "who," "what," "when," and "where" from a truly multimodal perspective, leveraging audiovisual(More)
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