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Minimum rank error training for language modeling
Discriminative training techniques have been successfully developed for many pattern recognition applications. In speech recognition, discriminative training aims to minimize the metric of word error rate. However, in an information retrieval system, the best performance should be achieved by maximizing the average precision. In this paper, we construct the discriminative n-gram language model for information retrieval following the metric of minimum rank error (MRE) rather than thedoi:10.21437/interspeech.2007-263 fatcat:xtq2wjjvnjggjgbbesamu56jde