@inproceedings{gao2005minimum,
author = {Gao, Jianfeng and Yu, Hao and Yuan, Wei and Xu, Peng},
title = {Minimum Sample Risk Methods for Language Modeling},
booktitle = {HLT/EMNLP},
year = {2005},
month = {October},
abstract = {This paper proposes a new discriminative training method, called minimum sample risk (MSR), of estimating parameters of language models for text input. While most existing discriminative training methods use a loss function that can be optimized easily but approaches only approximately to the objective of minimum error rate, MSR minimizes the training error directly using a heuristic training procedure. Evaluations on the task of Japanese text input show that MSR can handle a large number of features and training samples; it significantly outperforms a regular trigram model trained using maximum likelihood estimation, and it also outperforms the two widely applied discriminative methods, the boosting and the perceptron algorithms, by a small but statistically significant margin.},
url = {http://approjects.co.za/?big=en-us/research/publication/minimum-sample-risk-methods-language-modeling/},
edition = {HLT/EMNLP},
}