SOLD: Sinhala offensive language dataset: SOLD: Sinhala offensive language dataset: T. Ranasinghe et al.
The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural language processing (NLP) communities, motivating the development of various systems trained to detect potentially har...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 1; pp. 297 - 338 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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Springer Nature
Mar2025
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| Materias: | |
| 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=183750671&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 183750671 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2025 vid: 59 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 183750671 10.1007/s10579-024-09723-1 ppf: 297 ppct: 41 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.3MB tig: atl: SOLD: Sinhala offensive language dataset: SOLD: Sinhala offensive language dataset: T. Ranasinghe et al. aug: au: Ranasinghe, Tharindu Anuradha, Isuri Premasiri, Damith Silva, Kanishka Hettiarachchi, Hansi Uyangodage, Lasitha Zampieri, Marcos affil: https://ror.org/05j0ve876 Aston University, Birmingham, UK https://ror.org/01k2y1055 University of Wolverhampton, Wolverhampton, UK https://ror.org/00t67pt25 Birmingham City University, Birmingham, UK https://ror.org/00pd74e08 University of Müanster, Münster, Germany https://ror.org/02jqj7156 George Mason University, Fairfax, VA, USA su: Low-resource languages Natural language processing Machine learning Artificial intelligence Internet content sug: subj: Low-resource languages Natural language processing Machine learning Artificial intelligence Internet content keyword: Communication and Culture Linguistics Information and Computing Sciences Artificial Intelligence and Image Processing Deep learning Language Offensive language identification Transformers ab: The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural language processing (NLP) communities, motivating the development of various systems trained to detect potentially harmful content automatically. These systems require annotated datasets to train the machine learning (ML) models. However, with a few notable exceptions, most datasets on this topic have dealt with English and a few other high-resource languages. As a result, the research in offensive language identification has been limited to these languages. This paper addresses this gap by tackling offensive language identification in Sinhala, a low-resource Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments on this dataset. SOLD is a manually annotated dataset containing 10,000 posts from Twitter annotated as offensive and not offensive at both sentence-level and token-level, improving the explainability of the ML models. SOLD is the first large publicly available offensive language dataset compiled for Sinhala. We also introduce SemiSOLD, a larger dataset containing more than 145,000 Sinhala tweets, annotated following a semi-supervised approach. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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