TitleOpen Domain Event Extraction from Twitter
Publication TypeConference Paper
Year of Publication2012
AuthorsRitter A, Mausam, Etzioni O, Clark S
Conference NameKDD
Date or Month PublishedAugust 14
Conference LocationBeijing, China
AbstractTweets 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.
Downloadshttp://www.cs.washington.edu/homes/aritter/rt080-ritter.pdf PDF
Month of PublicationAugust
Citation Key8171