SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions

Miguel A. García-Cumbreras, Salud María Jiménez-Zafra, Arturo Montejo-Ráez, Manuel Carlos Díaz-Galiano, Estela Saquete


Abstract
This paper describes the participation of the SINAI-DL team at RumourEval (Task 7 in SemEval 2019, subtask A: SDQC). SDQC addresses the challenge of rumour stance classification as an indirect way of identifying potential rumours. Given a tweet with several replies, our system classifies each reply into either supporting, denying, questioning or commenting on the underlying rumours. We have applied data augmentation, temporal expressions labelling and transfer learning with a four-layer neural classifier. We achieve an accuracy of 0.715 with the official run over reply tweets.
Anthology ID:
S19-2196
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1120–1124
Language:
URL:
https://aclanthology.org/S19-2196
DOI:
10.18653/v1/S19-2196
Bibkey:
Cite (ACL):
Miguel A. García-Cumbreras, Salud María Jiménez-Zafra, Arturo Montejo-Ráez, Manuel Carlos Díaz-Galiano, and Estela Saquete. 2019. SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1120–1124, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
SINAI-DL at SemEval-2019 Task 7: Data Augmentation and Temporal Expressions (García-Cumbreras et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2196.pdf