Date of Original Version



Conference Proceeding

Abstract or Description

Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream. The TDT Pilot Study ran from September 1996 through October 1997. The primary participants were DARPA, Carnegie Mellon University, Dragon Systems, and the University of Massachusetts at Amherst. This report summarizes the findings of the pilot study. The TDT work continues in a new project involving larger training and test corpora, more active participants, and a more broadly defined notion of “topic” than was used in the pilot study.

The following individuals participated in the research reported.

James Allan, UMass

Brian Archibald, CMU

Doug Beeferman, CMU

Adam Berger, CMU

Ralf Brown, CMU

Jaime Carbonell, CMU

Ira Carp, Dragon

Bruce Croft, UMass,

George Doddington, DARPA

Larry Gillick, Dragon

Alex Hauptmann, CMU

John Lafferty, CMU

Victor Lavrenko, UMass

Xin Liu, CMU

Steve Lowe, Dragon

Paul van Mulbregt, Dragon

Ron Papka, UMass

Thomas Pierce, CMU

Jay Ponte, UMass

Mike Scudder, UMass

Charles Wayne, DARPA

Jon Yamron, Dragon

Yiming Yang, CMU