From Language to Family and Back: Native Language and Language Family Identification from English Text

Ariel Stolerman, Aylin Caliskan, Rachel Greenstadt

Abstract: Revealing an anonymous author’s traits from text is a well-researched area. In this paper we aim to identify the native language and language family of a non-native English author, given his/her English writings. We extract features from the text based on prior work, and extend or modify it to construct different feature sets, and use support vector machines for classification. We show that native language identification accuracy can be improved by up to 6.43% for a 9-class task, depending on the feature set, by introducing a novel method to incorporate language family information. In addition we show that introducing grammarbased features improves accuracy of both native language and language family identification.