Abstract
In this paper, we present our submission to the SemEval 2022 - Task 4 on Patronizing and Condescending Language (PCL) detection. Weapproach this problem as a traditional text classification problem with machine learning (ML)methods. We experiment and investigate theuse of various ML algorithms for detecting PCL in news articles. Our best methodology achieves an F1- Score of 0.39 for subtask1 witha rank of 63 out of 80, and F1-score of 0.082for subtask2 with a rank of 41 out of 48 on the blind dataset provided in the shared task.- Anthology ID:
- 2022.semeval-1.48
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 369–373
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.48
- DOI:
- 10.18653/v1/2022.semeval-1.48
- Cite (ACL):
- Abhishek Singh. 2022. Team LEGO at SemEval-2022 Task 4: Machine Learning Methods for PCL Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 369–373, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Team LEGO at SemEval-2022 Task 4: Machine Learning Methods for PCL Detection (Singh, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2022.semeval-1.48.pdf