DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Author(s)Vijayaraghavan, Prashanth; Sysoev, Ivan Sergeevich; Vosoughi, Soroush; Roy, Deb K
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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 character level 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.
DepartmentMassachusetts Institute of Technology. Media Laboratory; Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Proceedings of 10th International Workshop on Semantic Evaluation (SemEval-2016)
Association for Computational Linguistics
Vijayaraghavan, Prashanth, Ivan Sysoev, Soroush Vosoughi, and Deb Roy. "DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs." Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), June 16-17, 2016, San Diego, California, ACL, pp.413-419.
Author's final manuscript