About: BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering
Creator	Krallinger, Martin Krithara, Anastasia Nentidis, Anastasios Paliouras, Georgios Villegas, Marta
Source	PMC
abstract	This paper describes the eighth edition of the BioASQ Challenge, which will run as an evaluation Lab in the context of CLEF2020. 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.
has issue date	2020-03-24(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45442-5_71
has license	no-cc
sha1sum (hex)	f3f32973c8687de8fcdda44166476207278d220e
schema:url	https://doi.org/10.1007/978-3-030-45442-5_71
resource representing a document's title	BioASQ at CLEF2020: Large-Scale Biomedical Semantic Indexing and Question Answering
has PubMed Central identifier	PMC7148078
schema:publication	Advances in Information Retrieval
resource representing a document's body	covid:f3f32973c8687de8fcdda44166476207278d220e#body_text
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