Abstract
As an important task in Topic Detection and Tracking (TDT), New Event Detection (NED) aims to monitor the stream of news stories and detect new events reported by the first story of a topic. This paper proposes a Temporal Topic Model (TTM) to describe a topic as a series of events corresponding to different time. NED, based on TTM, firstly identifies whether a story includes the same time expressions with old topics, and then it verifies whether the story and the topics include relevant events corresponding to the expressions. Thus, a story will be determined as a new event, if it includes much few simultaneous relevant events of old topics. Additionally, this paper analyzes the distribution of time expressions to identify both seminal and novel events, by which NED modifies the probabilities of stories to be new events based on whether they include seminal or novel events of old topics. We compare our methods with some existing NED systems on TDT4 corpus, which demonstrate our methods substantially improve the efficiency and accuracy of NED.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computer Processing of Languages
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
