CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset Article Swipe

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Ting-Hao Huang , Chieh-Yang Huang , Chien‐Kuang Cornelia Ding , Yen-Chia Hsu , C. Lee Giles ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2005.02367

This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10,966 English abstracts in the COVID-19 Open Research Dataset. CODA-19 was created by 248 crowd workers from Amazon Mechanical Turk within 10 days, and achieved labeling quality comparable to that of experts. Each abstract was annotated by nine different workers, and the final labels were acquired by majority vote. The inter-annotator agreement (Cohen's kappa) between the crowd and the biomedical expert (0.741) is comparable to inter-expert agreement (0.788). CODA-19's labels have an accuracy of 82.2% when compared to the biomedical expert's labels, while the accuracy between experts was 85.0%. Reliable human annotations help scientists access and integrate the rapidly accelerating coronavirus literature, and also serve as the battery of AI/NLP research, but obtaining expert annotations can be slow. We demonstrated that a non-expert crowd can be rapidly employed at scale to join the fight against COVID-19.

Related Topics

Computer Science

Crowdsourcing

Artificial Intelligence

Infectious Diseases (Medical Specialty)

Disease

Concepts

Coda Coronavirus disease 2019 (COVID-19) Computer science Crowdsourcing Artificial intelligence Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Quality (philosophy) 2019-20 coronavirus outbreak Natural language processing Information retrieval World Wide Web Medicine Seismology Virology Outbreak Pathology Philosophy Epistemology Infectious disease (medical specialty) Disease Geology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2005.02367
PDF: https://arxiv.org/pdf/2005.02367
OA Status: green
Cited By: 11
Related Works: 10
OpenAlex ID: https://openalex.org/W4287781395

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OpenAlex ID: https://openalex.org/W4287781395

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DOI: https://doi.org/10.48550/arxiv.2005.02367

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Title: CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset

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Type: preprint

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Language: en

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Publication year: 2020

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Publication date: 2020-05-05

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Authors: Ting-Hao Huang, Chieh-Yang Huang, Chien‐Kuang Cornelia Ding, Yen-Chia Hsu, C. Lee Giles

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Landing page: https://arxiv.org/abs/2005.02367

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PDF URL: https://arxiv.org/pdf/2005.02367

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Concepts: Coda, Coronavirus disease 2019 (COVID-19), Computer science, Crowdsourcing, Artificial intelligence, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Quality (philosophy), 2019-20 coronavirus outbreak, Natural language processing, Information retrieval, World Wide Web, Medicine, Seismology, Virology, Outbreak, Pathology, Philosophy, Epistemology, Infectious disease (medical specialty), Disease, Geology

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Cited by: 11

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Citations by year (recent): 2025: 1, 2023: 5, 2022: 3, 2021: 2

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abstract_inverted_index.annotated	53
abstract_inverted_index.different	56
abstract_inverted_index.integrate	114
abstract_inverted_index.obtaining	130
abstract_inverted_index.research,	128
abstract_inverted_index.Mechanical	36
abstract_inverted_index.biomedical	77, 97
abstract_inverted_index.comparable	45, 81
abstract_inverted_index.introduces	2
abstract_inverted_index.non-expert	140
abstract_inverted_index.scientists	111
abstract_inverted_index.Background,	10
abstract_inverted_index.annotations	109, 132
abstract_inverted_index.coronavirus	118
abstract_inverted_index.literature,	119
abstract_inverted_index.accelerating	117
abstract_inverted_index.demonstrated	137
abstract_inverted_index.inter-expert	83
abstract_inverted_index.human-annotated	5
abstract_inverted_index.inter-annotator	68
abstract_inverted_index.Finding/Contribution,	13
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	5
citation_normalized_percentile