OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language models Article Swipe
Ying Luo
,
Lis Kanashiro Pereira
,
Ichiro Kobayashi
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.smm4h-1.25
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.18653/v1/2021.smm4h-1.25
Since the outbreak of coronavirus at the end of 2019, there have been numerous studies on coro- navirus in the NLP arena. Meanwhile, Twitter has been a valuable source of news and a pub- lic medium for the conveyance of information and personal expression. This paper describes the system developed by the Ochadai team for the Social Media Mining for Health Appli- cations (SMM4H) 2021 Task 5, which aims to automatically distinguish English tweets that self-report potential cases of COVID-19 from those that do not. We proposed a model ensemble that leverages pre-trained represen- tations from COVID-Twitter-BERT (Müller et al., 2020), RoBERTa (Liu et al., 2019), and Twitter-RoBERTa (Glazkova et al., 2021). Our model obtained F1-scores of 76% on the test set in the evaluation phase, and 77.5% in the post-evaluation phase.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Task (project management)
Social media
Computer science
Set (abstract data type)
Artificial intelligence
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
F1 score
Sentiment analysis
Natural language processing
Test set
Test (biology)
World Wide Web
Medicine
Engineering
Biology
Pathology
Disease
Infectious disease (medical specialty)
Paleontology
Systems engineering
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18653/v1/2021.smm4h-1.25
- https://aclanthology.org/2021.smm4h-1.25.pdf
- OA Status
- gold
- Cited By
- 1
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3172360059
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3172360059Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18653/v1/2021.smm4h-1.25Digital Object Identifier
- Title
-
OCHADAI at SMM4H-2021 Task 5: Classifying self-reporting tweets on potential cases of COVID-19 by ensembling pre-trained language modelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Ying Luo, Lis Kanashiro Pereira, Ichiro KobayashiList of authors in order
- Landing page
-
https://doi.org/10.18653/v1/2021.smm4h-1.25Publisher landing page
- PDF URL
-
https://aclanthology.org/2021.smm4h-1.25.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://aclanthology.org/2021.smm4h-1.25.pdfDirect OA link when available
- Concepts
-
Coronavirus disease 2019 (COVID-19), Task (project management), Social media, Computer science, Set (abstract data type), Artificial intelligence, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), F1 score, Sentiment analysis, Natural language processing, Test set, Test (biology), World Wide Web, Medicine, Engineering, Biology, Pathology, Disease, Infectious disease (medical specialty), Paleontology, Systems engineering, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2021: 1Per-year citation counts (last 5 years)
- References (count)
-
5Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.that | 74, 82, 90 |
| abstract_inverted_index.2019, | 9 |
| abstract_inverted_index.77.5% | 127 |
| abstract_inverted_index.Media | 57 |
| abstract_inverted_index.Since | 0 |
| abstract_inverted_index.cases | 77 |
| abstract_inverted_index.coro- | 16 |
| abstract_inverted_index.model | 88, 113 |
| abstract_inverted_index.paper | 45 |
| abstract_inverted_index.there | 10 |
| abstract_inverted_index.those | 81 |
| abstract_inverted_index.which | 67 |
| abstract_inverted_index.2019), | 105 |
| abstract_inverted_index.2020), | 100 |
| abstract_inverted_index.2021). | 111 |
| abstract_inverted_index.Appli- | 61 |
| abstract_inverted_index.Health | 60 |
| abstract_inverted_index.Mining | 58 |
| abstract_inverted_index.Social | 56 |
| abstract_inverted_index.arena. | 21 |
| abstract_inverted_index.medium | 35 |
| abstract_inverted_index.phase, | 125 |
| abstract_inverted_index.phase. | 131 |
| abstract_inverted_index.source | 28 |
| abstract_inverted_index.system | 48 |
| abstract_inverted_index.tweets | 73 |
| abstract_inverted_index.(SMM4H) | 63 |
| abstract_inverted_index.English | 72 |
| abstract_inverted_index.Ochadai | 52 |
| abstract_inverted_index.RoBERTa | 101 |
| abstract_inverted_index.Twitter | 23 |
| abstract_inverted_index.cations | 62 |
| abstract_inverted_index.navirus | 17 |
| abstract_inverted_index.studies | 14 |
| abstract_inverted_index.tations | 94 |
| abstract_inverted_index.(Müller | 97 |
| abstract_inverted_index.COVID-19 | 79 |
| abstract_inverted_index.ensemble | 89 |
| abstract_inverted_index.numerous | 13 |
| abstract_inverted_index.obtained | 114 |
| abstract_inverted_index.outbreak | 2 |
| abstract_inverted_index.personal | 42 |
| abstract_inverted_index.proposed | 86 |
| abstract_inverted_index.valuable | 27 |
| abstract_inverted_index.(Glazkova | 108 |
| abstract_inverted_index.F1-scores | 115 |
| abstract_inverted_index.describes | 46 |
| abstract_inverted_index.developed | 49 |
| abstract_inverted_index.leverages | 91 |
| abstract_inverted_index.potential | 76 |
| abstract_inverted_index.represen- | 93 |
| abstract_inverted_index.Meanwhile, | 22 |
| abstract_inverted_index.conveyance | 38 |
| abstract_inverted_index.evaluation | 124 |
| abstract_inverted_index.coronavirus | 4 |
| abstract_inverted_index.distinguish | 71 |
| abstract_inverted_index.expression. | 43 |
| abstract_inverted_index.information | 40 |
| abstract_inverted_index.pre-trained | 92 |
| abstract_inverted_index.self-report | 75 |
| abstract_inverted_index.automatically | 70 |
| abstract_inverted_index.Twitter-RoBERTa | 107 |
| abstract_inverted_index.post-evaluation | 130 |
| abstract_inverted_index.COVID-Twitter-BERT | 96 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.53585413 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |