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Feature distributions in exponential language models

Considering of the features' distribution but not just the counts of features' appearances in sequence makes exponential language models more powerful to capture the global language phenomena. This paper constructs an exponential language model with binary variables' distributions of features, and uses minimum sample risk training method to train model by utilizing more features and adjusting their parameters. In this paper we show that the language model trained on Chinese Internet chat corpus, obtains up to 19% sentence correct rate improvement and up to 7.46% Chinese character correct rate improvement when compared to the baseline model.