Abstract
This paper describes our system created for the SemEval-2018 Task 3: Irony detection in English tweets. Our strongly constrained system uses only the provided training data without any additional external resources. Our system is based on Maximum Entropy classifier and various features using parse tree, POS tags, and morphological features. Even without additional lexicons and word embeddings we achieved fourth place in Subtask A and seventh in Subtask B in terms of accuracy.- Anthology ID:
- S18-1084
- 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:
- 520–524
- Language:
- URL:
- https://aclanthology.org/S18-1084
- DOI:
- 10.18653/v1/S18-1084
- Cite (ACL):
- Tomáš Hercig. 2018. UWB at SemEval-2018 Task 3: Irony detection in English tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 520–524, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- UWB at SemEval-2018 Task 3: Irony detection in English tweets (Hercig, SemEval 2018)
- PDF:
- https://preview.aclanthology.org/landing_page/S18-1084.pdf