Abstract
In this paper, we discuss our models applied to Task 4: Human Value Detection of SemEval 2023, which incorporated two different embedding techniques to interpret the data. Preliminary experiments were conducted to observe important word types. Subsequently, we explored an XGBoost model, an unsupervised learning model, and two Ensemble learning models were then explored. The best performing model, an ensemble model employing a soft voting technique, secured the 34th spot out of 39 teams, on a class imbalanced dataset. We explored the inclusion of different parts of the provided knowledge resource and found that considering only specific parts assisted our models.- Anthology ID:
- 2023.semeval-1.29
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 205–211
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.29
- DOI:
- 10.18653/v1/2023.semeval-1.29
- Cite (ACL):
- Ethan Heavey, Milton King, and James Hughes. 2023. StFX-NLP at SemEval-2023 Task 4: Unsupervised and Supervised Approaches to Detecting Human Values in Arguments. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 205–211, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- StFX-NLP at SemEval-2023 Task 4: Unsupervised and Supervised Approaches to Detecting Human Values in Arguments (Heavey et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.semeval-1.29.pdf