@inproceedings{LiuLiDemarestetal.2017,
  author    = {Liu, Can and Li, Wen and Demarest, Bradford and Chen, Yue and Couture, Sara and Dakota, Daniel and Haduong, Nikita and Kaufmann, Noah and Lamont, Andrew and Pancholi, Manan and Steimel, Kenneth and K{\"u}bler, Sandra},
  title     = {IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter},
  booktitle = {Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). San Diego, California. June 16-17, 2016},
  isbn      = {978-1-941643-95-2},
  url       = {https://aclweb.org/anthology/S/S16/},
  pages     = {394 -- 400},
  year      = {2017},
  abstract  = {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.},
  subject      = {Syntaktische Analyse},
  language  = {en}
}