A maximum entropy approach to natural language processing
Computational Linguistics
Inducing Features of Random Fields
Inducing Features of Random Fields
Computational Linguistics - Special issue on using large corpora: I
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Extracting word correspondences from bilingual corpora based on word co-occurrences information
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Structural feature selection for English-Korean statistical machine translation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Extracting word sequence correspondences with support vector machines
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
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This paper proposes a learning method of translation rules from parallel corpora. This method applies the maximum entropy principle to a probabilistic model of translation rules. First, we define feature functions which express statistical properties of this model. Next, in order to optimize the model, the system iterates following steps: (1) selects a feature function which maximizes loglikelihood, and (2) adds this function to the model incrementally. As computational cost associated with this model is too expensive, we propose several methods to suppress the overhead in order to realize the system. The result shows that it attained 69.54% recall rate.