We propose a system for the Topic Detection and Tracking (TDT) detection task concerned with the unsupervised grouping of news stories according to topic. We use an incremental k -means algorithm for clustering stories. For comparing stories, we utilize a probabilistic document similarity metric and a traditional vector-space metric. We note that that the clustering algorithm requires two different types of metrics and adapt similarity metrics for each purpose. The system achieves a topic-weighted miss rate of 12% at a false accept rate of 0.22%.
Cite as: Walls, F., Jin, H., Sista, S., Schwartz, R. (1999) Topic detection in broadcast news. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 2451-2454, doi: 10.21437/Eurospeech.1999-539
@inproceedings{walls99_eurospeech,
author={Frederick Walls and Hubert Jin and Sreenivasa Sista and Richard Schwartz},
title={{Topic detection in broadcast news}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={2451--2454},
doi={10.21437/Eurospeech.1999-539}
}