R. Manmatha
James Allan
ABSTRACT
Topic Detection and Tracking (TDT) tasks are evaluated using a cost function. The standard TDT cost function assumes a constant probability of relevance P(rel) across all topics. In practice, P(rel) varies widely across topics. We argue using both theoretical and experimental evidence that the cost function should be modified to account for the varying P(rel).
- A. Arampatzis and A. van Hameren. The score-distributional threshold optimization for adaptive binary classification tasks. In the Proc. of the 24th ACM SIGIR conf., pages 285--293, Sept 2001. Google Scholar
Digital Library
- J. Fiscus and G. R. Doddington. Topic detection and tracking evaluation overview. In J. Allan, editor, Topic Detection and Tracking: Event-based Information Organization, pages 17--31. Kluwer Academic Publishers, 2002. Google ScholarDigital Library
- R. Manmatha, T. Rath, and F. Feng. Modeling score distributions for combining the outputs of search engines. In the Proc. of the 24th ACM SIGIR conf. on Research and Developement in Information Retrieval, pages 267--275, Sept 2001. Google ScholarDigital Library
- A. Martin, T. K. G. Doddington, M. Ordowski, and M. Przybocki. The DET curve in assessment of detection task performance. In Proceedings of EuroSpeech'97, volume 4, pages 1895--1898, 1997.Google Scholar
Index Terms
- A critical examination of TDT's cost function
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