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CICLING
2010
Springer

Identification of Translationese: A Machine Learning Approach

11 years 3 months ago
Identification of Translationese: A Machine Learning Approach
This paper presents a machine learning approach to the study of translationese. The goal is to train a computer system to distinguish between translated and non-translated text, in order to determine the characteristic features that influence the classifiers. Several algorithms reach up to 97.62% success rate on a technical dataset. Moreover, the SVM classifier consistently reports a statistically significant improved accuracy when the learning system benefits from the addition of simplification features to the basic translational classifier system. Therefore, these findings may be considered an argument for the existence of the Simplification Universal.
Iustina Ilisei, Diana Inkpen, Gloria Corpas Pastor
Added 02 Sep 2010
Updated 02 Sep 2010
Type Conference
Year 2010
Where CICLING
Authors Iustina Ilisei, Diana Inkpen, Gloria Corpas Pastor, Ruslan Mitkov
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