ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods

Zhenghang Yin, Feixiang Wang, Man Lan, Wenting Wang


Abstract
The paper describes our submissions to task 3 in SemEval-2018. There are two subtasks: Subtask A is a binary classification task to determine whether a tweet is ironic, and Subtask B is a fine-grained classification task including four classes. To address them, we explored supervised machine learning method alone and in combination with neural networks.
Anthology ID:
S18-1098
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
600–606
Language:
URL:
https://aclanthology.org/S18-1098
DOI:
10.18653/v1/S18-1098
Bibkey:
Cite (ACL):
Zhenghang Yin, Feixiang Wang, Man Lan, and Wenting Wang. 2018. ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 600–606, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods (Yin et al., SemEval 2018)
Copy Citation:
PDF:
https://aclanthology.org/S18-1098.pdf