DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, Deb Kumar Roy · 2016
This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6).We utilized recent advances in short text categorization using deep learning to create word-level and character-level models.The choice between word-level and characterlevel models in each particular case was informed through validation performance.Our final system is a combination of classifiers using word-level or character-level models.We also employed novel data augmentation techniques to expand and diversify our training dataset, thus making our system more robust.Our system achieved a macro-average precision, recall and F1-scores of 0.67, 0.61 and 0.635 respectively.