OLID-BR: offensive language identification dataset for Brazilian Portuguese.
Social media has revolutionized the manner in which our society is interconnected. While this extensive connectivity offers numerous benefits, it is also accompanied by significant drawbacks, particularly in terms of the proliferation of fake news and the vast dissemination of hate speech. Identifyi...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 4; pp. 1263 - 1290 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Dec2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=180627299&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 180627299 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2024 vid: 58 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 180627299 10.1007/s10579-023-09657-0 ppf: 1263 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.4MB tig: atl: OLID-BR: offensive language identification dataset for Brazilian Portuguese. aug: au: Trajano, Douglas Bordini, Rafael H. Vieira, Renata affil: https://ror.org/025vmq686 School of Technology, Pontifical Catholic University of Rio Grande do Sul - PUCRS, Porto Alegre, Brazil https://ror.org/02gyps716 CIDEHUS, University of Evora, Évora, Portugal su: Natural language processing Hate speech Portuguese language Contextual analysis Fake news sug: subj: Natural language processing Hate speech Portuguese language Contextual analysis Fake news keyword: BERT Content moderation systems Dataset NER NLP Offensive comments Offensive language detection OLID-BR Toxic spans detection Toxicity detection systems ab: Social media has revolutionized the manner in which our society is interconnected. While this extensive connectivity offers numerous benefits, it is also accompanied by significant drawbacks, particularly in terms of the proliferation of fake news and the vast dissemination of hate speech. Identifying offensive comments is a critical task for ensuring the safety of users, which is why industry and academia have been working on developing solutions to this problem. Prior research on hate speech detection has predominantly focused on the English language, with few studies devoted to other languages such as Portuguese. This paper introduces the Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR), a high-quality NLP dataset for offensive language detection, which we make publicly available. The dataset contains 6,354 (extendable to 13,538) comments labeled using a fine-grained three-layer annotation schema compatible with datasets in other languages, which allows the training of multilingual/cross-lingual models. The five NLP tasks available in OLID-BR allow the detection of offensive comments, the classification of the types of offenses such as racism, LGBTQphobia, sexism, xenophobia, and so on, the identification of the type and the target of offensive comments, and the extraction of toxic spans of offensive comments. All those tasks can enhance the capabilities of content moderation systems by providing deep contextual analysis or highlighting the spans that make a text toxic. We further experiment with and evaluate the dataset using state-of-the-art BERT-based and NER models, which demonstrates the usefulness of OLID-BR for the development of toxicity detection systems for Portuguese texts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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