Maximum Entropy Model Learning of the Translation Rules

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Maximum Entropy Model Learning of the Translation Rules
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.
Kengo Sato, Masakazu Nakanishi
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where ACL
Authors Kengo Sato, Masakazu Nakanishi
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