Subword-based Deep Averaging Networks for Author Profiling in Social Media

Author profiling aims at identifying the authors’ traits on the basis of their sociolect aspect, that is, how language is shared by them. This work describes the system submitted by Symanto Research for the PAN 2017 Author Profiling Shared Task. The current edition is focused on language variety and gender identification on Twitter. We address these tasks by exploiting the morphology and semantics of the words. For that purpose, we generate embeddings of the authors’ text based on subword character n-grams. These representations are classified using deep averaging networks. Experimental results show competitive performance in the evaluated author profiling tasks.