| Abstract | Tweets are the most up-to-date and inclusive stream of in-
formation and commentary on current events, but they are
also fragmented and noisy, motivating the need for systems
that can extract, aggregate and categorize important events.
Previous work on extracting structured representations of
events has focused largely on newswire text; Twitter's unique
characteristics present new challenges and opportunities for
open-domain event extraction. This paper describes TwiCal|
the rst open-domain event-extraction and categorization
system for Twitter. We demonstrate that accurately ex-
tracting an open-domain calendar of signi cant events from
Twitter is indeed feasible. In addition, we present a novel
approach for discovering important event categories and clas-
sifying extracted events based on latent variable models. By
leveraging large volumes of unlabeled data, our approach
achieves a 14% increase in maximum F1 over a supervised
baseline. A continuously updating demonstration of our sys-
tem can be viewed at http://statuscalendar.com; Our
NLP tools are available at http://github.com/aritter/
twitter_nlp. |
