158 lines
57 KiB
JavaScript
158 lines
57 KiB
JavaScript
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||
'link-to-publication': 'http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.760.pdf',
|
||
'link-to-data': 'https://github.com/tommasoc80/AbuseEval',
|
||
'task-description': 'Explicitness annotation of offensive and abusive content',
|
||
'details-of-task': 'Enriched versions of the OffensEval/OLID dataset with the distinction of explicit/implicit offensive messages and the new dimension for abusive messages. Labels for offensive language: EXPLICIT, IMPLICT, NOT; Labels for abusive language: EXPLICIT, IMPLICT, NOTABU',
|
||
'size-of-dataset': 14100,
|
||
'percentage-abusive': 20.75,
|
||
language: 'English',
|
||
'level-of-annotation': ['Tweets'],
|
||
platform: ['Twitter'],
|
||
medium: ['Text'],
|
||
reference: 'Caselli, T., Basile, V., Jelena, M., Inga, K., and Michael, G. 2020. "I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language". The 12th Language Resources and Evaluation Conference (pp. 6193-6202). European Language Resources Association.'
|
||
}
|
||
}, {
|
||
_id: '80001dd32a752421fdcc64e91fbd237dc31d6bb3',
|
||
url_path: 'dataset-2',
|
||
file_path: 'content/dataset-2/index.md',
|
||
metadata: {
|
||
title: 'Abusive Language Detection on Arabic Social Media (Al Jazeera)',
|
||
'link-to-publication': 'https://www.aclweb.org/anthology/W17-3008',
|
||
'link-to-data': 'http://alt.qcri.org/~hmubarak/offensive/AJCommentsClassification-CF.xlsx',
|
||
'task-description': 'Ternary (Obscene, Offensive but not obscene, Clean)',
|
||
'details-of-task': 'Incivility',
|
||
'size-of-dataset': 32000,
|
||
'percentage-abusive': 0.81,
|
||
language: 'Arabic',
|
||
'level-of-annotation': ['Posts'],
|
||
platform: ['AlJazeera'],
|
||
medium: ['Text'],
|
||
reference: 'Mubarak, H., Darwish, K. and Magdy, W., 2017. Abusive Language Detection on Arabic Social Media. In: Proceedings of the First Workshop on Abusive Language Online. Vancouver, Canada: Association for Computational Linguistics, pp.52-56.'
|
||
}
|
||
}, {
|
||
_id: '96649d05d8193f4333b10015af76c6562971bd8c',
|
||
url_path: 'dataset-3',
|
||
file_path: 'content/dataset-3/index.md',
|
||
metadata: {
|
||
title: 'CoRAL: a Context-aware Croatian Abusive Language Dataset',
|
||
'link-to-publication': 'https://aclanthology.org/2022.findings-aacl.21/',
|
||
'link-to-data': 'https://github.com/shekharRavi/CoRAL-dataset-Findings-of-the-ACL-AACL-IJCNLP-2022',
|
||
'task-description': 'Multi-class based on context dependency categories (CDC)',
|
||
'details-of-task': 'Detectioning CDC from abusive comments',
|
||
'size-of-dataset': 2240,
|
||
'percentage-abusive': 100,
|
||
language: 'Croatian',
|
||
'level-of-annotation': ['Posts'],
|
||
platform: ['Posts'],
|
||
medium: ['Newspaper Comments'],
|
||
reference: 'Ravi Shekhar, Mladen Karan and Matthew Purver (2022). CoRAL: a Context-aware Croatian Abusive Language Dataset. Findings of the ACL: AACL-IJCNLP.'
|
||
}
|
||
}]
|
||
}
|
||
}`,...(Se=(Ne=WithoutFacets.parameters)==null?void 0:Ne.docs)==null?void 0:Se.source}}};var Me,Re,Pe;WithFacets.parameters={...WithFacets.parameters,docs:{...(Me=WithFacets.parameters)==null?void 0:Me.docs,source:{originalSource:`{
|
||
name: 'Catalog with facets',
|
||
args: {
|
||
datasets: [{
|
||
_id: '07026b22d49916754df1dc8ffb9ccd1c31878aae',
|
||
url_path: 'dataset-4',
|
||
file_path: 'content/dataset-4/index.md',
|
||
metadata: {
|
||
title: 'Detecting Abusive Albanian',
|
||
'link-to-publication': 'https://arxiv.org/abs/2107.13592',
|
||
'link-to-data': 'https://doi.org/10.6084/m9.figshare.19333298.v1',
|
||
'task-description': 'Hierarchical (offensive/not; untargeted/targeted; person/group/other)',
|
||
'details-of-task': 'Detect and categorise abusive language in social media data',
|
||
'size-of-dataset': 11874,
|
||
'percentage-abusive': 13.2,
|
||
language: 'Albanian',
|
||
'level-of-annotation': ['Posts'],
|
||
platform: ['Instagram', 'Youtube'],
|
||
medium: ['Text'],
|
||
reference: 'Nurce, E., Keci, J., Derczynski, L., 2021. Detecting Abusive Albanian. arXiv:2107.13592'
|
||
}
|
||
}, {
|
||
_id: '42c86cf3c4fbbab11d91c2a7d6dcb8f750bc4e19',
|
||
url_path: 'dataset-1',
|
||
file_path: 'content/dataset-1/index.md',
|
||
metadata: {
|
||
title: 'AbuseEval v1.0',
|
||
'link-to-publication': 'http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.760.pdf',
|
||
'link-to-data': 'https://github.com/tommasoc80/AbuseEval',
|
||
'task-description': 'Explicitness annotation of offensive and abusive content',
|
||
'details-of-task': 'Enriched versions of the OffensEval/OLID dataset with the distinction of explicit/implicit offensive messages and the new dimension for abusive messages. Labels for offensive language: EXPLICIT, IMPLICT, NOT; Labels for abusive language: EXPLICIT, IMPLICT, NOTABU',
|
||
'size-of-dataset': 14100,
|
||
'percentage-abusive': 20.75,
|
||
language: 'English',
|
||
'level-of-annotation': ['Tweets'],
|
||
platform: ['Twitter'],
|
||
medium: ['Text'],
|
||
reference: 'Caselli, T., Basile, V., Jelena, M., Inga, K., and Michael, G. 2020. "I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language". The 12th Language Resources and Evaluation Conference (pp. 6193-6202). European Language Resources Association.'
|
||
}
|
||
}, {
|
||
_id: '80001dd32a752421fdcc64e91fbd237dc31d6bb3',
|
||
url_path: 'dataset-2',
|
||
file_path: 'content/dataset-2/index.md',
|
||
metadata: {
|
||
title: 'Abusive Language Detection on Arabic Social Media (Al Jazeera)',
|
||
'link-to-publication': 'https://www.aclweb.org/anthology/W17-3008',
|
||
'link-to-data': 'http://alt.qcri.org/~hmubarak/offensive/AJCommentsClassification-CF.xlsx',
|
||
'task-description': 'Ternary (Obscene, Offensive but not obscene, Clean)',
|
||
'details-of-task': 'Incivility',
|
||
'size-of-dataset': 32000,
|
||
'percentage-abusive': 0.81,
|
||
language: 'Arabic',
|
||
'level-of-annotation': ['Posts'],
|
||
platform: ['AlJazeera'],
|
||
medium: ['Text'],
|
||
reference: 'Mubarak, H., Darwish, K. and Magdy, W., 2017. Abusive Language Detection on Arabic Social Media. In: Proceedings of the First Workshop on Abusive Language Online. Vancouver, Canada: Association for Computational Linguistics, pp.52-56.'
|
||
}
|
||
}, {
|
||
_id: '96649d05d8193f4333b10015af76c6562971bd8c',
|
||
url_path: 'dataset-3',
|
||
file_path: 'content/dataset-3/index.md',
|
||
metadata: {
|
||
title: 'CoRAL: a Context-aware Croatian Abusive Language Dataset',
|
||
'link-to-publication': 'https://aclanthology.org/2022.findings-aacl.21/',
|
||
'link-to-data': 'https://github.com/shekharRavi/CoRAL-dataset-Findings-of-the-ACL-AACL-IJCNLP-2022',
|
||
'task-description': 'Multi-class based on context dependency categories (CDC)',
|
||
'details-of-task': 'Detectioning CDC from abusive comments',
|
||
'size-of-dataset': 2240,
|
||
'percentage-abusive': 100,
|
||
language: 'Croatian',
|
||
'level-of-annotation': ['Posts'],
|
||
platform: ['Posts'],
|
||
medium: ['Newspaper Comments'],
|
||
reference: 'Ravi Shekhar, Mladen Karan and Matthew Purver (2022). CoRAL: a Context-aware Croatian Abusive Language Dataset. Findings of the ACL: AACL-IJCNLP.'
|
||
}
|
||
}],
|
||
facets: ['language', 'platform']
|
||
}
|
||
}`,...(Pe=(Re=WithFacets.parameters)==null?void 0:Re.docs)==null?void 0:Pe.source}}};const __namedExportsOrder=["WithoutFacets","WithFacets"];export{WithFacets,WithoutFacets,__namedExportsOrder,meta as default};
|
||
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