Abstract
BBN’s systems for TDT use probabilistic models for higher accuracy and easy training. They generate measures that are normalized across topics, so that only one threshold is necessary to make decisions. These systems make little or no use of deep linguistic knowledge, and therefore are easy to modify for new languages and domains. At the same time their performance has consistently been in the top tier.
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References
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© 2002 Springer Science+Business Media New York
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Leek, T., Schwartz, R., Sista, S. (2002). Probabilistic Approaches to Topic Detection and Tracking. In: Allan, J. (eds) Topic Detection and Tracking. The Information Retrieval Series, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-0933-2_4
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DOI: https://doi.org/10.1007/978-1-4615-0933-2_4
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