Name
Title | ||
|---|---|---|
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 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.18653/v1/S16-1067 | SemEval@NAACL-HLT |
DocType | Volume | Citations |
Conference | abs/1606.05694 | 9 |
PageRank | References | Authors |
0.59 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prashanth Vijayaraghavan | 1 | 47 | 6.20 |
| Ivan Sysoev | 2 | 9 | 0.59 |
| soroush vosoughi | 3 | 50 | 9.78 |
| Deb Roy | 4 | 1033 | 92.10 |