Jan 27, 2016

Citizen Science for Mining the Biomedical Literature

BioRxiv : the Preprint Server for Biology
Ginger TsuengAndrew I Su

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 freeflowing 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 nonexperts 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 Matrice...Continue Reading

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Mentioned in this Paper

Study
Mechanical Treatments
Size
Exertion
Human Volunteers
Indopan
Research Personnel
Nucleotide Excision Repair
Extraction
Naming, Function

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