log in  |  register  |  feedback?  |  help  |  web accessibility
Towards Automated Fact Discovery and Ranking
Wednesday, September 18, 2019, 11:00 am-12:00 pm Calendar
  • You are subscribed to this talk through .
  • You are watching this talk through .
  • You are subscribed to this talk. (unsubscribe, watch)
  • You are watching this talk. (unwatch, subscribe)
  • You are not subscribed to this talk. (watch, subscribe)
In this talk, I present the work of finding new, prominent situational facts, which are emerging statements about objects that stand out within certain contexts. Many such facts are newsworthy—e.g., an athlete’s outstanding performance in a game, or a viral video’s impressive popularity. Effective and efficient identification of these facts assists journalists in reporting, one of the main goals of computational journalism. A situational fact can be modeled as a “contextual” tuple that stands out against historical tuples in a context, specified by a conjunctive constraint involving dimension attributes when a set of measure attributes are compared. New tuples are constantly added to the table, reflecting events happening in the real world. Our goal is to discover constraint-measure pairs that qualify a new tuple as a contextual significant tuple, and discover them quickly before the event becomes yesterday’s news.

Naeemul Hassan is an assistant professor in the Philip Merrill College of Journalism and the College of Information Studies of the University of Maryland, College Park. He has interests in research areas related to Database, Data Mining, and Natural Language Processing. His current research focus is on Computational Journalism and Social Sensing. Before joining UMCP, he was an assistant professor in the Computer and Information Science department at the University of Mississippi. He earned a doctoral degree in computer science from the University of Texas at Arlington in 2016.

This talk is organized by Doug Oard