Document Model with Attention Bidirectional Recurrent Network for Gender Identification

Abstract

Author profiling is an important statistical and semantic processing task Author profiling is an important statistical and semantic processing task in the field of natural language processing (NLP). It refers to the extraction of information from author’s texts such as gender, age and other kinds of personality traits. Author profiling can be applied in various fields like marketing, security and forensics. In this work, we explore how bi-directional deep learning architectures can be used to learn the abstract and higher-level features of the document, which could be employed to identify the author’s gender. To deal with this, we extend Bidirectional Long Short-Term Memory Networks Language Models with an attention mechanism. The originality of our approach lays in its ability to capture the most important semantic information in a sentence. The experimental results on Facebook and twitter corpus show that our method outperformed the majority of the existing methods.