Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm

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• dc.contributor.author Pérez-Pérez, Martin
• dc.contributor.author Pérez-Rodríguez, Gael
• dc.contributor.author Blanco-Míguez, Aitor
• dc.contributor.author Fdez-Riverola, Florentino
• dc.contributor.author Valencia, Alfonso
• dc.contributor.author Krallinger, Martin
• dc.contributor.author Lourenço, Anália
• dc.date.accessioned 2022-05-16T10:34:04Z
• dc.date.available 2022-05-16T10:34:04Z
• dc.date.issued 2019
• dc.description.abstract Background: Shared tasks and community challenges represent key instruments to promote research, collaboration and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such tasks relied on the comparison of automatically generated results against a so-called Gold Standard dataset of manually labelled textual data, regardless of efficiency and robustness of the underlying implementations. Due to the rapid growth of unstructured data collections, including patent databases and particularly the scientific literature, there is a pressing need to generate, assess and expose robust big data text mining solutions to semantically enrich documents in real time. To address this pressing need, a novel track called "Technical interoperability and performance of annotation servers" was launched under the umbrella of the BioCreative text mining evaluation effort. The aim of this track was to enable the continuous assessment of technical aspects of text annotation web servers, specifically of online biomedical named entity recognition systems of interest for medicinal chemistry applications. Results: A total of 15 out of 26 registered teams successfully implemented online annotation servers. They returned predictions during a two-month period in predefined formats and were evaluated through the BeCalm evaluation platform, specifically developed for this track. The track encompassed three levels of evaluation, i.e. data format considerations, technical metrics and functional specifications. Participating annotation servers were implemented in seven different programming languages and covered 12 general entity types. The continuous evaluation of server responses accounted for testing periods of low activity and moderate to high activity, encompassing overall 4,092,502 requests from three different document provider settings. The median response time was below 3.74 s, with a median of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process over 100,000 requests in a 5-day period. Conclusions: The presented track was a novel experimental task that systematically evaluated the technical performance aspects of online entity recognition systems. It raised the interest of a significant number of participants. Future editions of the competition will address the ability to process documents in bulk as well as to annotate full-text documents.
• dc.description.sponsorship This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 654021 (OpenMinTeD), and the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology for funding. This work was partially supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia), under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, and the Portuguese Foundation for Science and Technology (FCT), under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684)
• dc.format.mimetype application/pdf
• dc.identifier.citation Pérez-Pérez M, Pérez-Rodríguez G, Blanco-Míguez A, Fdez-Riverola F. Valencia A, Krallinger M et al. Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm. J Cheminform. 2019 Jun 24;11(1):42. DOI:10.1186/s13321-019-0363-6
• dc.identifier.doi http://dx.doi.org/10.1186/s13321-019-0363-6
• dc.identifier.issn 1758-2946
• dc.identifier.uri http://hdl.handle.net/10230/53092
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/654021
• dc.rights © Martin Pérez-Pérez et al. 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.other Química clínica
• dc.subject.other Investigació
• dc.subject.other Innovacions tecnològiques
• dc.title Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion