Minimum verification error training for topic verification
Kuo, H.-K.J.
Chin-Hui Lee
Zitouni, I.
Fosler-Lussiert, E.
Lucent Technol. Bell Labs., Murray Hill, NJ, USA;
Abstract
We propose a new formulation of minimum verification error training and apply it to the problem of topic verification as an example. In topic verification, a decision is made as to whether a document truly belongs to a particular topic of interest. Such a decision typically depends on a comparison between a model for the desired topic and a model for background topics, using a decision threshold. We propose modeling the background topics as a cohort model consisting of a weighted combination of the M closest topics discovered from the training data. The weights and the decision threshold are optimized using the generalized probabilistic descent algorithm to explicitly minimize the verification error rate, which is defined to be a weighted sum of the Type I (false rejection) and Type II (false acceptance) errors.
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