UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training Data Article Swipe
Arun Kumar Rajendran
,
Chiyu Zhang
,
Muhammad Abdul-Mageed
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.18653/v1/s19-2136
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.18653/v1/s19-2136
We examine learning offensive content on Twitter with limited, imbalanced data. For the purpose, we investigate the utility of using various data enhancement methods with a host of classical ensemble classifiers. Among the 75 participating teams in SemEval-2019 sub-task B, our system ranks 6th (with 0.706 macro F1-score). For sub-task C, among the 65 participating teams, our system ranks 9th (with 0.587 macro F1-score).
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/s19-2136
- https://www.aclweb.org/anthology/S19-2136.pdf
- OA Status
- gold
- References
- 17
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2948877201
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W2948877201Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.18653/v1/s19-2136Digital Object Identifier
- Title
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UBC-NLP at SemEval-2019 Task 6: Ensemble Learning of Offensive Content With Enhanced Training DataWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-01-01Full publication date if available
- Authors
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Arun Kumar Rajendran, Chiyu Zhang, Muhammad Abdul-MageedList of authors in order
- Landing page
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https://doi.org/10.18653/v1/s19-2136Publisher landing page
- PDF URL
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https://www.aclweb.org/anthology/S19-2136.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.aclweb.org/anthology/S19-2136.pdfDirect OA link when available
- Concepts
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SemEval, Offensive, Task (project management), Macro, Computer science, Artificial intelligence, Natural language processing, Ensemble learning, Machine learning, Training set, Host (biology), Mathematics, Operations research, Biology, Economics, Management, Programming language, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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17Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.For | 11, 48 |
| abstract_inverted_index.our | 40, 56 |
| abstract_inverted_index.the | 12, 16, 32, 52 |
| abstract_inverted_index.data | 21 |
| abstract_inverted_index.host | 26 |
| abstract_inverted_index.with | 7, 24 |
| abstract_inverted_index.(with | 44, 60 |
| abstract_inverted_index.0.587 | 61 |
| abstract_inverted_index.0.706 | 45 |
| abstract_inverted_index.Among | 31 |
| abstract_inverted_index.among | 51 |
| abstract_inverted_index.data. | 10 |
| abstract_inverted_index.macro | 46, 62 |
| abstract_inverted_index.ranks | 42, 58 |
| abstract_inverted_index.teams | 35 |
| abstract_inverted_index.using | 19 |
| abstract_inverted_index.system | 41, 57 |
| abstract_inverted_index.teams, | 55 |
| abstract_inverted_index.Twitter | 6 |
| abstract_inverted_index.content | 4 |
| abstract_inverted_index.examine | 1 |
| abstract_inverted_index.methods | 23 |
| abstract_inverted_index.utility | 17 |
| abstract_inverted_index.various | 20 |
| abstract_inverted_index.ensemble | 29 |
| abstract_inverted_index.learning | 2 |
| abstract_inverted_index.limited, | 8 |
| abstract_inverted_index.purpose, | 13 |
| abstract_inverted_index.sub-task | 38, 49 |
| abstract_inverted_index.classical | 28 |
| abstract_inverted_index.offensive | 3 |
| abstract_inverted_index.F1-score). | 47, 63 |
| abstract_inverted_index.imbalanced | 9 |
| abstract_inverted_index.enhancement | 22 |
| abstract_inverted_index.investigate | 15 |
| abstract_inverted_index.SemEval-2019 | 37 |
| abstract_inverted_index.classifiers. | 30 |
| abstract_inverted_index.participating | 34, 54 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.05014019 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |