Abstract
A logistic regression model only fed with character and word n-grams is proposed for the SemEval-2022 Task 4 on Patronizing and Condescending Language Detection (PCL). It obtained an average level of performance, well above the performance of a system that tries to guess without using any knowledge about the task, but much lower than the best teams. To facilitate the interpretation of the performance scores, the F1 measure, the best level of performance of a system that tries to guess without using any knowledge is calculated and used to correct the F1 scores in the manner of a Kappa. As the proposed model is very similar to the one that performed well on a task requiring to automatically identify hate speech and offensive content, this paper confirms the difficulty of PCL detection.- Anthology ID:
- 2022.semeval-1.67
- 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:
- 490–495
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.67
- DOI:
- 10.18653/v1/2022.semeval-1.67
- Cite (ACL):
- Yves Bestgen. 2022. SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 490–495, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- SATLab at SemEval-2022 Task 4: Trying to Detect Patronizing and Condescending Language with only Character and Word N-grams (Bestgen, SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.semeval-1.67.pdf