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--- |
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language: |
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- en |
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- fr |
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- de |
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- nl |
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- es |
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bigbio_language: |
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- English |
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- French |
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- German |
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- Dutch |
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- Spanish |
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license: gpl-3.0 |
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bigbio_license_shortname: GPL_3p0_ONLY |
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multilinguality: multilingual |
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pretty_name: MantraGSC |
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homepage: https://github.com/mi-erasmusmc/Mantra-Gold-Standard-Corpus |
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bigbio_pubmed: true |
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bigbio_public: true |
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bigbio_tasks: |
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- NAMED_ENTITY_RECOGNITION |
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- NAMED_ENTITY_DISAMBIGUATION |
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--- |
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# Dataset Card for Mantra GSC |
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## Dataset Description |
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- **Homepage:** https://github.com/mi-erasmusmc/Mantra-Gold-Standard-Corpus |
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- **Pubmed:** True |
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- **Public:** True |
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- **Tasks:** NER, NED |
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We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. |
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## Citation Information |
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``` |
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@article{10.1093/jamia/ocv037, |
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author = {Kors, Jan A and Clematide, Simon and Akhondi, |
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Saber A and van Mulligen, Erik M and Rebholz-Schuhmann, Dietrich}, |
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title = "{A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC}", |
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journal = {Journal of the American Medical Informatics Association}, |
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volume = {22}, |
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number = {5}, |
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pages = {948-956}, |
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year = {2015}, |
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month = {05}, |
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abstract = "{Objective To create a multilingual gold-standard corpus for biomedical concept recognition.Materials |
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and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, |
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biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language |
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independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and |
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covering a wide range of semantic groups. To reduce the annotation workload, automatically generated |
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preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and |
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cross-language consistency checks were carried out to arrive at the final annotations.Results The number of final |
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annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are |
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similar to those between individual annotators and the gold standard. The automatically generated harmonized |
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annotation set for each language performed equally well as the best annotator for that language.Discussion The use |
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of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation |
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efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance |
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of automatic annotation techniques.Conclusion To our knowledge, this is the first gold-standard corpus for |
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biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety |
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of semantic groups that are being covered, and the diversity of text genres that were annotated.}", |
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issn = {1067-5027}, |
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doi = {10.1093/jamia/ocv037}, |
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url = {https://doi.org/10.1093/jamia/ocv037}, |
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eprint = {https://academic.oup.com/jamia/article-pdf/22/5/948/34146393/ocv037.pdf}, |
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} |
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``` |
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