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- Title
Maximum Entropy Modeling: A Suitable Framework to Learn Context-Dependent Lexicon Models for Statistical Machine Translation.
- Authors
Ismael García-Varea; Francisco Casacuberta
- Abstract
Abstract Current statistical machine translation systems are mainly based on statistical word lexicons. However, these models are usually context-independent, therefore, the disambiguation of the translation of a source word must be carried out using other probabilistic distributions (distortion distributions and statistical language models). One efficient way to add contextual information to the statistical lexicons is based on maximum entropy modeling. In that framework, the context is introduced through feature functions that allow us to automatically learn context-dependent lexicon models.
- Publication
Machine Learning, 2005, Vol 60, Issue 1-3, p135
- ISSN
0885-6125
- Publication type
Article
- DOI
10.1007/s10994-005-0915-z