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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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Metadaten
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
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
Dewey Decimal Classification:400 Sprache / 400 Sprache, Linguistik
Linguistics-Classification:Computerlinguistik
Open Access?:Ja
Licence (German):Es gilt das UrhG