TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Liu, Can
A1  - Li, Wen
A1  - Demarest, Bradford
A1  - Chen, Yue
A1  - Couture, Sara
A1  - Dakota, Daniel
A1  - Haduong, Nikita
A1  - Kaufmann, Noah
A1  - Lamont, Andrew
A1  - Pancholi, Manan
A1  - Steimel, Kenneth
A1  - Kübler, Sandra
T1  - IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter
T2  - Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). San Diego, California. June 16-17, 2016
N2  - 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.
KW  - Syntaktische Analyse
Y1  - 2016
U6  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-61835
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:mh39-61835
UR  - https://aclweb.org/anthology/S/S16/
SN  - 978-1-941643-95-2
SB  - 978-1-941643-95-2
SP  - 394
EP  - 400
PB  - Association for Computational Linguistics
CY  - Stroudsburg, PA
ER  -