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--- |
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language: |
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- sv |
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bigbio_language: |
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- Swedish |
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license: cc-by-sa-4.0 |
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multilinguality: monolingual |
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bigbio_license_shortname: CC_BY_SA_4p0 |
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pretty_name: Swedish Medical NER |
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homepage: https://github.com/olofmogren/biomedical-ner-data-swedish/ |
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bigbio_pubmed: False |
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bigbio_public: True |
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bigbio_tasks: |
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- NAMED_ENTITY_RECOGNITION |
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--- |
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# Dataset Card for Swedish Medical NER |
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## Dataset Description |
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|
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- **Homepage:** https://github.com/olofmogren/biomedical-ner-data-swedish/ |
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- **Pubmed:** False |
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- **Public:** True |
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- **Tasks:** NER |
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swedish_medical_ner is Named Entity Recognition dataset on medical text in Swedish. |
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It consists three subsets which are in turn derived from three different sources |
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respectively: the Swedish Wikipedia (a.k.a. wiki), Läkartidningen (a.k.a. lt), |
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and 1177 Vårdguiden (a.k.a. 1177). While the Swedish Wikipedia and Läkartidningen |
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subsets in total contains over 790000 sequences with 60 characters each, |
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the 1177 Vårdguiden subset is manually annotated and contains 927 sentences, |
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2740 annotations, out of which 1574 are disorder and findings, 546 are |
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pharmaceutical drug, and 620 are body structure. |
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Texts from both Swedish Wikipedia and Läkartidningen were automatically annotated |
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using a list of medical seed terms. Sentences from 1177 Vårdguiden were manuually |
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annotated. |
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## Citation Information |
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``` |
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@inproceedings{almgren-etal-2016-named, |
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author = { |
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Almgren, Simon and |
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Pavlov, Sean and |
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Mogren, Olof |
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}, |
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title = {Named Entity Recognition in Swedish Medical Journals with Deep Bidirectional Character-Based LSTMs}, |
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booktitle = {Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM 2016)}, |
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publisher = {The COLING 2016 Organizing Committee}, |
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pages = {30-39}, |
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year = {2016}, |
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month = {12}, |
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url = {https://aclanthology.org/W16-5104}, |
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eprint = {https://aclanthology.org/W16-5104.pdf} |
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} |
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``` |
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