Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
10K<n<100K
License:
Commit
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Parent(s):
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Update license, repo, summary, citation metadata
Browse files
README.md
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language:
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- en
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license:
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multilinguality:
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- monolingual
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size_categories:
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## Dataset Description
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- **Homepage:** [linnaeus](http://linnaeus.sourceforge.net/)
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- **Repository:**
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- **Paper:** [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85)
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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LINNAEUS
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### Supported Tasks and Leaderboards
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### Licensing Information
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[
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### Citation Information
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```bibtex
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@article{Gerner2010,
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year = {2010}
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}
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```
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language:
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- en
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license:
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- cc-by-4.0
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multilinguality:
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- monolingual
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size_categories:
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## Dataset Description
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- **Homepage:** [linnaeus](http://linnaeus.sourceforge.net/)
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- **Repository:** https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/linnaeus-IOB
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- **Paper:** [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85)
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- **Leaderboard:**
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- **Point of Contact:**
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### Dataset Summary
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The LINNAEUS corpus consists of 100 full-text documents from the PMCOA
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document set which were randomly selected. All mentions of species terms were manually
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annotated and normalized to the NCBI taxonomy IDs of the intended species.
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The original LINNAEUS corpus is available in a TAB-separated standoff format. The resource does not define training,
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development or test subsets.
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We converted the corpus into BioNLP shared task standoff format using a custom script, split it into 50-, 17-, and 33-
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document training, development and test sets, and then converted these into the CoNLL format using standoff2conll.
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As a full-text corpus, LINNAEUS contains comparatively frequent
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non-ASCII characters, which were mapped to ASCII using the
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standoff2conll -a option.
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The conversion was highly accurate, but due to sentence-splitting errors within entity mentions,
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the number of annotations in the converted data was larger by four (100.09%) than that
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in the source data. 99.77% of names in the original annotation matched names in the converted
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data.
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### Supported Tasks and Leaderboards
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### Licensing Information
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This version of the dataset is licensed under [Creative Commons Attribution 4.0 International](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/blob/master/LICENSE.md).
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### Citation Information
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```bibtex
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@article{crichton2017neural,
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title={A neural network multi-task learning approach to biomedical named entity recognition},
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author={Crichton, Gamal and Pyysalo, Sampo and Chiu, Billy and Korhonen, Anna},
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journal={BMC Bioinformatics},
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volume={18},
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number={1},
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pages={368},
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year={2017},
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publisher={BioMed Central}
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doi = {10.1186/s12859-017-1776-8},
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issn = {1471-2105},
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url = {https://doi.org/10.1186/s12859-017-1776-8},
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}
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@article{Gerner2010,
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author = {Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
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doi = {10.1186/1471-2105-11-85},
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issn = {1471-2105},
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journal = {BMC Bioinformatics},
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number = {1},
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pages = {85},
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title = {{LINNAEUS: A species name identification system for biomedical literature}},
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url = {https://doi.org/10.1186/1471-2105-11-85},
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volume = {11},
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year = {2010}
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}
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```
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