Please use this identifier to cite or link to this item: https://oa.tib.eu/renate/handle/123456789/6142
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dc.rights.licenseCC BY 4.0 Unportedeng
dc.contributor.authorD'Souza, Jennifer-
dc.contributor.authorHoppe, Anett-
dc.contributor.authorBrack, Arthur-
dc.contributor.authorJaradeh, Mohamad Yaser-
dc.contributor.authorAuer, Sören-
dc.contributor.authorEwerth, Ralph-
dc.date.accessioned2021-04-12T13:18:11Z-
dc.date.available2021-04-12T13:18:11Z-
dc.date.issued2020-
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/6142-
dc.identifier.urihttps://doi.org/10.34657/5190-
dc.description.abstractWe introduce the STEM (Science, Technology, Engineering, and Medicine) Dataset for Scientific Entity Extraction, Classification, and Resolution, version 1.0 (STEM-ECR v1.0). The STEM-ECR v1.0 dataset has been developed to provide a benchmark for the evaluation of scientific entity extraction, classification, and resolution tasks in a domain-independent fashion. It comprises abstracts in 10 STEM disciplines that were found to be the most prolific ones on a major publishing platform. We describe the creation of such a multidisciplinary corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism for scientific entities in a multidisciplinary scientific context; 2) the feasibility of the domain-independent human annotation of scientific entities under such a generic formalism; 3) a performance benchmark obtainable for automatic extraction of multidisciplinary scientific entities using BERT-based neural models; 4) a delineated 3-step entity resolution procedure for human annotation of the scientific entities via encyclopedic entity linking and lexicographic word sense disambiguation; and 5) human evaluations of Babelfy returned encyclopedic links and lexicographic senses for our entities. Our findings cumulatively indicate that human annotation and automatic learning of multidisciplinary scientific concepts as well as their semantic disambiguation in a wide-ranging setting as STEM is reasonable.eng
dc.language.isoengeng
dc.publisherParis : European Language Resources Associationeng
dc.relation.ispartofProceedings of the 12th Language Resources and Evaluation Conference (LREC 2020)eng
dc.relation.urihttps://www.aclweb.org/anthology/2020.lrec-1.268-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.subjectEntity Recognitioneng
dc.subjectEntity Classificationeng
dc.subjectEntity Resolutioneng
dc.subjectEntity Linkingeng
dc.subjectWord Sense Disambiguationeng
dc.subjectEvaluation Corpuseng
dc.subjectLanguage Resourceeng
dc.subject.ddc020eng
dc.titleThe STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sourceseng
dc.typebookParteng
dc.typeconferenceObjecteng
dc.typeTexteng
dc.description.versionpublishedVersioneng
wgl.contributorTIBeng
wgl.subjectErziehung, Schul-und Bildungsweseneng
wgl.typeBuchkapitel / Sammelwerksbeitrageng
wgl.typeKonferenzbeitrageng
dc.bibliographicCitation.firstPage2192eng
dc.bibliographicCitation.lastPage2203eng
tib.accessRightsopenAccesseng
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D’Souza, Jennifer, Anett Hoppe, Arthur Brack, Mohamad Yaser Jaradeh, Sören Auer and Ralph Ewerth, 2020. The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. In: . Paris : European Language Resources Association
D’Souza, J., Hoppe, A., Brack, A., Jaradeh, M. Y., Auer, S. and Ewerth, R. (2020) “The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources.” Paris : European Language Resources Association.
D’Souza J, Hoppe A, Brack A, Jaradeh M Y, Auer S, Ewerth R. The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. Paris : European Language Resources Association; 2020.
D’Souza, J., Hoppe, A., Brack, A., Jaradeh, M. Y., Auer, S., & Ewerth, R. (2020). The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. Paris : European Language Resources Association.
D’Souza J, Hoppe A, Brack A, Jaradeh M Y, Auer S, Ewerth R. The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources. In: Paris : European Language Resources Association; 2020.


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