Name
Abstract | ||
|---|---|---|
This paper describes our submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors. We participated in both subtasks. The goal of subtask A is to classify the type of interaction between a rumorous social media post and a reply post as support, query, deny, or comment. The goal of subtask B is to predict the veracity of a given rumor. For subtask A, we implement a CNN-based neural architecture using ELMo embeddings of post text combined with auxiliary features and achieve a F1-score of 44.6%. For subtask B, we employ a MLP neural network leveraging our estimates for subtask A and achieve a F1-score of 30.1% (second place in the competition). We provide results and analysis of our system performance and present ablation experiments. |
Year | Venue | Field |
|---|---|---|
2019 | North American Chapter of the Association for Computational Linguistics | Architecture,Social media,SemEval,Computer science,Rumor,Artificial intelligence,Natural language processing,Artificial neural network |
DocType | Volume | Citations |
Journal | abs/1904.03084 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ipek Baris | 1 | 0 | 0.68 |
| Lukas Schmelzeisen | 2 | 0 | 0.34 |
| Steffen Staab | 3 | 6658 | 593.89 |