DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
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.
Keywords
Cite
@article{arxiv.1606.05694,
title = {DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs},
author = {Prashanth Vijayaraghavan and Ivan Sysoev and Soroush Vosoughi and Deb Roy},
journal= {arXiv preprint arXiv:1606.05694},
year = {2016}
}
Comments
SemEval 2016, San Diego, California. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). San Diego, California