DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs

摘要

This paper describes our approach for the Detecting Stance in Tweets task(SemEval-2016 Task 6). We utilized recent advances in short text categorizationusing deep learning to create word-level and character-level models. The choicebetween word-level and character-level models in each particular case wasinformed through validation performance. Our final system is a combination ofclassifiers using word-level or character-level models. We also employed noveldata augmentation techniques to expand and diversify our training dataset, thusmaking our system more robust. Our system achieved a macro-average precision,recall and F1-scores of 0.67, 0.61 and 0.635 respectively.