Abstract
A robust comprehension of sarcasm detection iscritical for creating artificial systems that can ef-fectively perform sentiment analysis in writtentext. In this work, we investigate AI approachesto identifying whether a text is sarcastic or notas part of SemEval-2022 Task 6. We focus oncreating systems for Task A, where we experi-ment with lightweight statistical classificationapproaches trained on both GloVe features andmanually-selected features. Additionally, weinvestigate fine-tuning the transformer modelBERT. Our final system for Task A is an Ex-treme Gradient Boosting Classifier trained onmanually-engineered features. Our final sys-tem achieved an F1-score of 0.2403 on SubtaskA and was ranked 32 of 43.- Anthology ID:
- 2022.semeval-1.129
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 919–922
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.129
- DOI:
- 10.18653/v1/2022.semeval-1.129
- Cite (ACL):
- Samantha Huang, Ethan Chi, and Nathan Chi. 2022. ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 919–922, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- ISD at SemEval-2022 Task 6: Sarcasm Detection Using Lightweight Models (Huang et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.semeval-1.129.pdf