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Identification of Translationese: A Machine Learning Approach

TitleIdentification of Translationese: A Machine Learning Approach
Publication TypeBook Chapter
Year of Publication2010
AuthorsIlisei, I, Inkpen, D, Corpas Pastor, G, Mitkov, R
EditorGelbukh, A
Book TitleComputational Linguistics and Intelligent Text Processing
Series TitleLecture Notes in Computer Science
CityBerlin / Heidelberg
ISBN Number978-3-642-12115-9

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.