Refine
Year of publication
- 2016 (1)
Document Type
Language
- English (1) (remove)
Has Fulltext
- yes (1)
Is part of the Bibliography
- no (1)
Keywords
Publicationstate
- Veröffentlichungsversion (1) (remove)
Reviewstate
- Peer-Review (1)
Publisher
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.