A Computational Linguistic Approach for Gender Prediction Based on Vietnamese Names

Abstract

Gender prediction is extensively studied in recent years since it is widely applied in many fields. Several factors have been investigated to determine a gender of male or female through facial images, voice, gait, finger print, etc. In this study, we present a machine learning approach for gender determination based on Vietnamese names. A model based on N-gram for the full name, combining its own middle name feature based on the specificity of Vietnamese language, is proposed. The experimental evaluation of gender prediction tasks is applied on GenderVN1.0 dataset (with 3 million Vietnamese names) that achieves 90.9% of accuracy.