Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology.
Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract r...
| Publicado en: | Language Resources & Evaluation Vol. 56; no. 3; pp. 807 - 856 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Springer Nature
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=158609433&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 158609433 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2022 vid: 56 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 158609433 10.1007/s10579-021-09553-5 ppf: 807 ppct: 49 formats: fmt: – @attributes: type: T – @attributes: type: P size: 10.9MB tig: atl: Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology. aug: au: Lücking, Andy Driller, Christine Stoeckel, Manuel Abrami, Giuseppe Pachzelt, Adrian Mehler, Alexander affil: TTLab, Goethe-University Frankfurt, Frankfurt, Germany Senckenberg – Leibniz Institution for Biodiversity and Earth System Research, Frankfurt, Germany University Library Johann Christian Senckenberg, Goethe-University Frankfurt, Frankfurt, Germany su: Biodiversity Annotations Data mining German language Linguistics Gene ontology sug: subj: Biodiversity Annotations Data mining German language Linguistics Gene ontology keyword: Annotation biofid Inter-annotator agreement Named entity recognition Semantic portal Specialized information service Taxon ab: Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract relevant information. To improve the access to semantic biodiversity information, we have launched the BIOfid project (www.biofid.de) and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. However, to make such a portal work, a couple of methods had to be developed or adapted first. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. Such methods draw on machine learning techniques, which in turn are trained by learning data. To this end, among others, we gathered the biofid text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. A special feature of biofid is its multiple annotation approach, which takes into account both general and biology-specific classifications, and by this means goes beyond previous, typically taxon- or ontology-driven proper name detection. We describe the design decisions and the genuine Annotation Hub Framework underlying the biofid annotations and present agreement results. The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. Finally, some general lessons, in particular with multiple annotation projects, are drawn. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2022. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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