Topic detection in broadcast news

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%. 1. Introduction Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative concerned with finding groups of stories on the same topic. It consists of three tasks: segmentation, tracking, and detection. We focus on the detection task, which is involved with the unsupervised grouping of stories that are on the same topic. Story groupings are created through clustering, a technique that can be used to assign each story to one and only o...