Abstract
This paper presents the experimentation of systems for detecting online sexism relying on classical models, deep learning models, and transformer-based models. The systems aim to provide a comprehensive approach to handling the intricacies of online language, including slang and neologisms. The dataset consists of labeled and unlabeled data from Gab and Reddit, which allows for the development of unsupervised or semi-supervised models. The system utilizes TF-IDF with classical models, bidirectional models with embedding, and pre-trained transformer models. The paper discusses the experimental setup and results, demonstrating the effectiveness of the system in detecting online sexism.- Anthology ID:
- 2023.semeval-1.101
- 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:
- 739–743
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.101
- DOI:
- 10.18653/v1/2023.semeval-1.101
- Cite (ACL):
- Efrat Luzzon and Chaya Liebeskind. 2023. JCT_DM at SemEval-2023 Task 10: Detection of Online Sexism: from Classical Models to Transformers. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 739–743, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- JCT_DM at SemEval-2023 Task 10: Detection of Online Sexism: from Classical Models to Transformers (Luzzon & Liebeskind, SemEval 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.semeval-1.101.pdf