Name
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. |
Year | Venue | Field |
|---|---|---|
2016 | SemEval@NAACL-HLT | SemEval,Ensemble forecasting,Computer science,Random subspace method,Precision and recall,Supervised learning,Artificial intelligence,Natural language processing,Random forest,Ensemble learning,Machine learning,Gradient boosting |
DocType | Citations | PageRank |
Conference | 7 | 0.51 |
References | Authors | |
9 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| can liu | 1 | 29 | 7.65 |
| wen li | 2 | 22 | 11.92 |
| Bradford Demarest | 3 | 7 | 1.18 |
| Yue Chen | 4 | 10 | 4.63 |
| Sara Couture | 5 | 7 | 0.51 |
| Daniel Dakota | 6 | 7 | 1.52 |
| Nikita Haduong | 7 | 7 | 0.51 |
| Noah Kaufman | 8 | 7 | 0.51 |
| Andrew Lamont | 9 | 7 | 0.85 |
| Manan Pancholi | 10 | 9 | 1.11 |
| Kenneth Steimel | 11 | 7 | 1.52 |
| Sandra Kübler | 12 | 56 | 13.29 |