DocumentCode
336818
Title
Probabilistic models for topic detection and tracking
Author
Walls, F. ; Jin, Hye-Jin ; Sista, S. ; Schwartz, R.
Author_Institution
BBN Syst. & Technol. Corp., Cambridge, MA, USA
Volume
1
fYear
1999
fDate
15-19 Mar 1999
Firstpage
521
Abstract
We present probabilistic models for use in detecting and tracking topics in broadcast news stories. Our information retrieval (IR) models are formally explained. The topic detection and tracking (TDT) initiative is discussed. The application of probabilistic models to the topic detection and tracking tasks is developed, and enhancements are discussed. We discuss four variations of these models, and we report our preliminary test results from the current TDT corpus
Keywords
computational linguistics; information retrieval; natural languages; broadcast news stories; enhancements; information retrieval models; probabilistic models; topic detection; topic detection and tracking; topic tracking; Broadcast technology; Broadcasting; Clustering algorithms; Information retrieval; Partitioning algorithms; Probability; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location
Phoenix, AZ
ISSN
1520-6149
Print_ISBN
0-7803-5041-3
Type
conf
DOI
10.1109/ICASSP.1999.758177
Filename
758177
Link To Document