@inproceedings{salas-jimenez-etal-2024-wikibias,
title = "{W}iki{B}ias as an Extrapolation Corpus for Bias Detection",
author = "Salas-Jimenez, K. and
Lopez-Ponce, Francisco Fernando and
Ojeda-Trueba, Sergio-Luis and
Bel-Enguix, Gemma",
editor = "Lucie-Aim{\'e}e, Lucie and
Fan, Angela and
Gwadabe, Tajuddeen and
Johnson, Isaac and
Petroni, Fabio and
van Strien, Daniel",
booktitle = "Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.wikinlp-1.10",
pages = "46--52",
abstract = "This paper explores whether it is possible to train a machine learning model using Wikipedia data to detect subjectivity in sentences and generalize effectively to other domains. To achieve this, we performed experiments with the WikiBias corpus, the BABE corpus, and the CheckThat! Dataset. Various classical models for ML were tested, including Logistic Regression, SVC, and SVR, including characteristics such as Sentence Transformers similarity, probabilistic sentiment measures, and biased lexicons. Pre-trained models like DistilRoBERTa, as well as large language models like Gemma and GPT-4, were also tested for the same classification task.",
}
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<abstract>This paper explores whether it is possible to train a machine learning model using Wikipedia data to detect subjectivity in sentences and generalize effectively to other domains. To achieve this, we performed experiments with the WikiBias corpus, the BABE corpus, and the CheckThat! Dataset. Various classical models for ML were tested, including Logistic Regression, SVC, and SVR, including characteristics such as Sentence Transformers similarity, probabilistic sentiment measures, and biased lexicons. Pre-trained models like DistilRoBERTa, as well as large language models like Gemma and GPT-4, were also tested for the same classification task.</abstract>
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%0 Conference Proceedings
%T WikiBias as an Extrapolation Corpus for Bias Detection
%A Salas-Jimenez, K.
%A Lopez-Ponce, Francisco Fernando
%A Ojeda-Trueba, Sergio-Luis
%A Bel-Enguix, Gemma
%Y Lucie-Aimée, Lucie
%Y Fan, Angela
%Y Gwadabe, Tajuddeen
%Y Johnson, Isaac
%Y Petroni, Fabio
%Y van Strien, Daniel
%S Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F salas-jimenez-etal-2024-wikibias
%X This paper explores whether it is possible to train a machine learning model using Wikipedia data to detect subjectivity in sentences and generalize effectively to other domains. To achieve this, we performed experiments with the WikiBias corpus, the BABE corpus, and the CheckThat! Dataset. Various classical models for ML were tested, including Logistic Regression, SVC, and SVR, including characteristics such as Sentence Transformers similarity, probabilistic sentiment measures, and biased lexicons. Pre-trained models like DistilRoBERTa, as well as large language models like Gemma and GPT-4, were also tested for the same classification task.
%U https://aclanthology.org/2024.wikinlp-1.10
%P 46-52
Markdown (Informal)
[WikiBias as an Extrapolation Corpus for Bias Detection](https://aclanthology.org/2024.wikinlp-1.10) (Salas-Jimenez et al., WikiNLP 2024)
ACL
- K. Salas-Jimenez, Francisco Fernando Lopez-Ponce, Sergio-Luis Ojeda-Trueba, and Gemma Bel-Enguix. 2024. WikiBias as an Extrapolation Corpus for Bias Detection. In Proceedings of the First Workshop on Advancing Natural Language Processing for Wikipedia, pages 46–52, Miami, Florida, USA. Association for Computational Linguistics.