DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
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vsvr_semeval2016.pdf
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Author(s)
Vijayaraghavan, Prashanth
Sysoev, Ivan Sergeevich
Vosoughi, Soroush
Roy, Deb K
Date Issued
June 2016
Journal
Proceedings of 10th International Workshop on Semantic Evaluation (SemEval-2016)
Publisher
Association for Computational Linguistics
Citation
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.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
http://aclweb.org/anthology/S/S16/S16-1067.pdf
