In this paper we present algorithms for story segmentation, topic detection, and topic tracking. The algorithmsuse a combination of machine learning, statistical naturallanguage processing and information retrieval techniques.The story segmentation algorithm is a two stage algorithm that uses a decision tree based probabilistic modelin the first stage and incorporates aspects of our topicdetection system via an information-retrieval based refinement scheme in the second stage. The topic detectionand tracking algorithm is an incremental clustering algorithm that employs a novel dynamic cluster-dependentsimilarity measure between documents and clusters. Per-formance of these algorithms are measured on the 1998DARPA sponsored Topic Detection and Tracking Phase2 (TDT2) evaluation task.
Cite as: Dharanipragada, S., Franz, M., McCarley, J.S., Roukos, S., Ward, T. (1999) Story segmentation and topic detection for recognized speech. Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999), 2435-2438, doi: 10.21437/Eurospeech.1999-535
@inproceedings{dharanipragada99_eurospeech,
author={S. Dharanipragada and Martin Franz and J. S. McCarley and Salim Roukos and T. Ward},
title={{Story segmentation and topic detection for recognized speech}},
year=1999,
booktitle={Proc. 6th European Conference on Speech Communication and Technology (Eurospeech 1999)},
pages={2435--2438},
doi={10.21437/Eurospeech.1999-535}
}