@inproceedings{luzzon-liebeskind-2023-jct,
title = "{JCT}{\_}{DM} at {S}em{E}val-2023 Task 10: Detection of Online Sexism: from Classical Models to Transformers",
author = "Luzzon, Efrat and
Liebeskind, Chaya",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.101",
doi = "10.18653/v1/2023.semeval-1.101",
pages = "739--743",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T JCT_DM at SemEval-2023 Task 10: Detection of Online Sexism: from Classical Models to Transformers
%A Luzzon, Efrat
%A Liebeskind, Chaya
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F luzzon-liebeskind-2023-jct
%X 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.
%R 10.18653/v1/2023.semeval-1.101
%U https://aclanthology.org/2023.semeval-1.101
%U https://doi.org/10.18653/v1/2023.semeval-1.101
%P 739-743
Markdown (Informal)
[JCT_DM at SemEval-2023 Task 10: Detection of Online Sexism: from Classical Models to Transformers](https://aclanthology.org/2023.semeval-1.101) (Luzzon & Liebeskind, SemEval 2023)
ACL