Abstract
This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.- Anthology ID:
- S18-1082
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venues:
- SemEval | *SEM
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 507–511
- Language:
- URL:
- https://aclanthology.org/S18-1082
- DOI:
- 10.18653/v1/S18-1082
- Cite (ACL):
- Kevin Swanberg, Madiha Mirza, Ted Pedersen, and Zhenduo Wang. 2018. ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 507–511, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- ALANIS at SemEval-2018 Task 3: A Feature Engineering Approach to Irony Detection in English Tweets (Swanberg et al., SemEval-*SEM 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S18-1082.pdf