Abstract
This paper describe the system we proposed to participate the first year of Irony detection in English tweets competition. Previous works demonstrate that LSTMs models have achieved remarkable performance in natural language processing; besides, combining multiple classification from various individual classifiers in general is more powerful than a single classification. In order to obtain more precision classification of irony detection, our system trained several individual neural network classifiers and combined their results according to the ensemble-learning algorithm.- Anthology ID:
- S18-1101
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 622–627
- Language:
- URL:
- https://aclanthology.org/S18-1101
- DOI:
- 10.18653/v1/S18-1101
- Cite (ACL):
- Bo Peng, Jin Wang, and Xuejie Zhang. 2018. YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 622–627, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- YNU-HPCC at SemEval-2018 Task 3: Ensemble Neural Network Models for Irony Detection on Twitter (Peng et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/S18-1101.pdf