Linguistic Features of Genre and Method Variation in Translation: A Computational Perspective
This work addresses genre and method variation in translation for computational linguistics, but it is incremental as it uses existing methods on a specific dataset.
The study applied a Bayesian classifier with linguistically motivated n-gram features to analyze genre and method variation in an English-German translation corpus, identifying key differences through feature analysis.
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.