GenSMT at SemEval-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approach

Dumitru Bogdan


Abstract
In this paper, we describe our participation in SemEval-2019 Task 3: EmoContext - A Shared Task on Contextual Emotion Detection in Text. We propose a three layer model with a generic, multi-purpose approach that without any task specific optimizations achieve competitive results (f1 score of 0.7096) in the EmoContext task. We describe our development strategy in detail along with an exposition of our results.
Anthology ID:
S19-2037
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
225–229
Language:
URL:
https://aclanthology.org/S19-2037
DOI:
10.18653/v1/S19-2037
Bibkey:
Cite (ACL):
Dumitru Bogdan. 2019. GenSMT at SemEval-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approach. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 225–229, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
GenSMT at SemEval-2019 Task 3: Contextual Emotion Detection in tweets using multi task generic approach (Bogdan, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2037.pdf
Data
EmoContext