Author Profiling with Word+Character Neural Attention Network

This paper describes neural network models that we prepared for the author profiling task of PAN@CLEF 2017. In previous PAN series, statistical models using a machine learning method with a variety of features have shown superior performances in author profiling tasks. We decided to tackle the author profiling task using neural networks. Neural networks have recently shown promising results in NLP tasks. Our models integrate word information and character information with multiple neural network layers. The proposed models have marked joint accuracies of 64–86% in the gender identification and the language variety identification of four languages.