In this paper we describe the use of text classification methods to investigate genre and method variation in an English - German translation corpus. For this purpose we use linguistically motivated features representing texts using a combination of part-of-speech tags arranged in bigrams, trigrams, and 4-grams. The classification method used in this paper is a Bayesian classifier with Laplace smoothing. We use the output of the classifiers to carry out an extensive feature analysis on the main difference between genres and methods of translation.
@article{arxiv.1709.04359,
title = {Linguistic Features of Genre and Method Variation in Translation: A Computational Perspective},
author = {Ekaterina Lapshninova-Koltunski and Marcos Zampieri},
journal= {arXiv preprint arXiv:1709.04359},
year = {2017}
}
Comments
To appear as a book chapter in Grammar of Genres and Styles. De Gruyter