Abstract
This paper describes the ninth edition of the BioASQ Challenge, which will run as an evaluation Lab in the context of CLEF2021. The aim of BioASQ is the promotion of systems and methods for highly precise biomedical information access. This is done through the organization of a series of challenges (shared tasks) on large-scale biomedical semantic indexing and question answering, where different teams develop systems that compete on the same demanding benchmark datasets that represent the real information needs of biomedical experts. In order to facilitate this information finding process, the BioASQ challenge introduced two complementary tasks: (a) the automated indexing of large volumes of unlabelled data, primarily scientific articles, with biomedical concepts, (b) the processing of biomedical questions and the generation of comprehensible answers. Rewarding the most competitive systems that outperform the state of the art, BioASQ manages to push the research frontier towards ensuring that the biomedical experts will have direct access to valuable knowledge.
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Notes
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IBECS includes bibliographic references from scientific articles in health sciences published in Spanish journals. http://ibecs.isciii.es.
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LILACS is the most important and comprehensive index of scientific and technical literature of Latin America and the Caribbean. It includes 26 countries, 882 journals and 878,285 records, 464,451 of which are full texts https://lilacs.bvsalud.org.
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Acknowledgments
Google is a proud sponsor of BioASQ in 2020. BioASQ is also sponsored by the Atypon Systems inc. BioASQ is grateful to NLM for providing the baselines for task 9a and to the CMU team for providing the baselines for task 9b. The MESINESP task is sponsored by the Spanish Plan for advancement of Language Technologies (Plan TL) and the Secretaría de Estado para el Avance Digital (SEAD). BioASQ is also grateful to LILACS, SCIELO and Biblioteca virtual en salud and Instituto de salud Carlos III for providing data for the MESINESP task.
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Krithara, A., Nentidis, A., Paliouras, G., Krallinger, M., Miranda, A. (2021). BioASQ at CLEF2021: Large-Scale Biomedical Semantic Indexing and Question Answering. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_73
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