Introduction
Topic Detection and Tracking (TDT) refers to automatic techniques for
finding topically related material in streams of data such as newswire and
broadcast news. The TDT3 corpus was created to support three TDT3 tasks: find
topically homogeneous sections (segmentation), detect the occurrence of new
events (detection), and track the reoccurrence of old or new events
(tracking). For further information on TDT3 please visit our TDT3
Information Pages.
Data
The TDT3 Multilanguage Text Corpus Version 2.0 is the first general release
of this collection (Version 1.0 was made available only to participants in the
TDT 1999 and 2000 evaluation tests). It contains data from the same nine
sources found in TDT2, plus two additional English television sources. Like
TDT2, it provides both manually-created and automatically-generated text for
most sources.
For TDT3, the daily collection took place over a period of three months
(October - December 1998). The sources and approximate number of stories per
source are as follows:
English sources Thousands of stories
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New York Times Newswire Service 6.9
Associated Press Worldstream Service 7.3
Cable News Network, "Headline News" 9.0
American Broadcasting Co., "World News Tonight" 1.0
Public Radio International, "The World" 1.6
Voice of America, English news programs 3.9
MS-NBC, "News with Brian Williams" 0.7
National Broadcasting Co., "NBC Nightly News" 0.8
Total English stories: 31.2 thousand
Mandarin sources
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Xinhua News Agency 5.2
Zaobao News Agency 3.8
Voice of America, Mandarin Chinese news programs 3.8
Total Mandarin stories: 12.8 thousand
The goal of Topic Detection and Tracking - Phase 3 (TDT3) is to create
core technology to monitor multiple streams of news in multiple languages and
media (newswire, radio, television, web sites or some future combination or
innovation), segmenting the streams into individual stories, detecting new
topics and tracking all stories discussing them. In additional to the TDT2
tasks of segmentation, detection and tracking, TDT3 adds the tasks of first
story detection and story-link detection. The goal of the latter is to detect
links between stories that discuss the same topic even though the topic has not
been defined in advance.
There are two types of files in this publication:
asr_sgm -- text data output from automatic speech recognition (ASR) systems
in English and Mandarin, formatted in "TIPSTER- style" SGML, derived from the
audio recordings of radio and TV broadcasts. To view a sample of an asr_sgm
file please go to example.asr_sgm.
tkn_sgm -- reference text data (newswire, closed captions and manual
transcripts), formatted in "TIPSTER-style" SGML. To view a sample of a tkn_sgm
file please go to example.tkn_sgm.
Updates
There are no updates at this time.
Content Copyright
Portions © 1998 American Broadcasting Company, The Associated Press, Cable News Network, LP, LLLP, National Broadcasting Company, Inc., New York Times, Public Radio International, SPH AsiaOne Ltd, Xinhua News Agency, © 1998-2001 Trustees of the University of Pennsylvania
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