IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter
- We present the IUCL system, based on supervised learning, for the shared task on stance detection. Our official submission, the random forest model, reaches a score of 63.60, and is ranked 6th out of 19 teams. We also use gradient boosting decision trees and SVM and merge all classifiers into an ensemble method. Our analysis shows that random forest is good at retrieving minority classes and gradient boosting majority classes. The strengths of different classifiers wrt. precision and recall complement each other in the ensemble.
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| Author: | Can Liu, Wen Li, Bradford Demarest, Yue Chen, Sara Couture, Daniel Dakota, Nikita Haduong, Noah Kaufmann, Andrew Lamont, Manan Pancholi, Kenneth Steimel, Sandra Kübler |
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| URN: | urn:nbn:de:bsz:mh39-61835 |
| URL: | https://aclweb.org/anthology/S/S16/ |
| ISBN: | 978-1-941643-95-2 |
| Parent Title (English): | Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). San Diego, California. June 16-17, 2016 |
| Publisher: | Association for Computational Linguistics |
| Place of publication: | Stroudsburg, PA |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of first Publication: | 2016 |
| Date of Publication (online): | 2017/05/23 |
| Publicationstate: | Veröffentlichungsversion |
| Reviewstate: | Peer-Review |
| GND Keyword: | Syntaktische Analyse |
| First Page: | 394 |
| Last Page: | 400 |
| DDC classes: | 400 Sprache / 400 Sprache, Linguistik |
| Open Access?: | ja |
| Linguistics-Classification: | Computerlinguistik |
| Licence (German): |  Urheberrechtlich geschützt |