Cross-linguistic authorship attribution and gender profiling. Machine translation as a method for bridging the language gap

This study explores the feasibility of cross-linguistic authorship attribution and the author’s gender identification using Machine Translation (MT). Computational stylistics experiments were conducted on a Greek blog corpus translated into English using Google’s Neural MT. A Random Forest algorithm was employed for authorship and gender profiling, using different feature groups [Author’s Multilevel N-gram Profiles, quantitative linguistics (QL), and cross-lingual word embeddings (CLWE)] in both original and translated texts. Results indicate that MT is a viable method for converting a multilingual corpus into one language for authorship attribution and gender profiling research, with considerable accuracy when training and testing datasets use identical language. In the pure cross-linguistic scenario, higher accuracies than the baselines were obtained using CLWE and QL features.

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Published in: Digital Scholarship in the Humanities
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1093/llc/fqae028