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Citizen Science for Mining the Biomedical Literature

Ginger Tsueng, View ORCID ProfileSteven M. Nanis, Jennifer Fouquier, View ORCID ProfileBenjamin M Good, View ORCID ProfileAndrew I Su
doi: https://doi.org/10.1101/038083
Ginger Tsueng
1Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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Steven M. Nanis
1Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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Jennifer Fouquier
1Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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Benjamin M Good
1Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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Andrew I Su
1Department of Molecular and Experimental Medicine, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA
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I. Abstract

Biomedical literature represents one of the largest and fastest growing collections of unstructured biomedical knowledge. Finding critical information buried in the literature can be challenging. In order to extract information from free-flowing text, researchers need to: 1. identify the entities in the text (named entity recognition), 2. apply a standardized vocabulary to these entities (normalization), and 3. identify how entities in the text are related to one another (relationship extraction.) Researchers have primarily approached these information extraction tasks through manual expert curation, and computational methods. We have previously demonstrated that named entity recognition (NER) tasks can be crowdsourced to a group of non-experts via the paid microtask platform, Amazon Mechanical Turk (AMT); and can dramatically reduce the cost and increase the throughput of biocuration efforts. However, given the size of the biomedical literature even information extraction via paid microtask platforms is not scalable. With our web-based application Mark2Cure (http://mark2cure.org). we demonstrate that NER tasks can also be performed by volunteer citizen scientists with high accuracy. We apply metrics from the Zooniverse Matrices of Citizen Science Success and provide the results here to serve as a basis of comparison for other citizen science projects. Further, we discuss design considerations, issues, and the application of analytics for successfully moving a crowdsourcing workflow from a paid microtask platform to a citizen science platform. To our knowledge, this study is the first application of citizen science to a natural language processing task.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 27, 2016.
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Citizen Science for Mining the Biomedical Literature
Ginger Tsueng, Steven M. Nanis, Jennifer Fouquier, Benjamin M Good, Andrew I Su
bioRxiv 038083; doi: https://doi.org/10.1101/038083
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Citizen Science for Mining the Biomedical Literature
Ginger Tsueng, Steven M. Nanis, Jennifer Fouquier, Benjamin M Good, Andrew I Su
bioRxiv 038083; doi: https://doi.org/10.1101/038083

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