Bidirectional LSTM for author gender identification

Abstract

Author profiling consists in inferring the authors’ gender, age, native language, dialects or personality by examining his/her written text. This important task is a very active research area because of its utility in crime, marketing and business. In this paper, we address the problem of gender identification by applying the Long Short-Term Memory neural network architecture. Which is a novel type of recurrent network architecture that implements an appropriate gradient-based learning algorithm to overcome the vanishing-gradient problem. Experimental results show that our composition outperformed the traditional machine learning methods on gender identification.