Identification of Translationese: A Machine Learning Approach

被引：0

作者：

Ilisei, Iustina ^{[1
]}

Inkpen, Diana ^{[2
]}

Pastor, Gloria Corpas ^{[3
]}

Mitkov, Ruslan ^{[1
]}

机构：

[1] Wolverhampton Univ, Res Inst Informat & Language Proc, Wolverhampton WV1 1DJ, W Midlands, England

[2] Univ Ottawa, Sch Informat Technol & Engn, Ottawa, ON K1N 6N5, Canada

[3] Univ Malaga, Dept Translat & Interpreting, E-29071 Malaga, Spain

来源：

COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING | 2010年 / 6008卷

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：503 / +

页数：3

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