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Dataset Summary

The LINNAEUS corpus consists of 100 full-text documents from the PMCOA document set which were randomly selected. All mentions of species terms were manually annotated and normalized to the NCBI taxonomy IDs of the intended species.

The original LINNAEUS corpus is available in a TAB-separated standoff format. The resource does not define training, development or test subsets.

We converted the corpus into BioNLP shared task standoff format using a custom script, split it into 50-, 17-, and 33- document training, development and test sets, and then converted these into the CoNLL format using standoff2conll.

As a full-text corpus, LINNAEUS contains comparatively frequent non-ASCII characters, which were mapped to ASCII using the standoff2conll -a option. The conversion was highly accurate, but due to sentence-splitting errors within entity mentions, the number of annotations in the converted data was larger by four (100.09%) than that in the source data. 99.77% of names in the original annotation matched names in the converted data.

Supported Tasks and Leaderboards

This dataset is used for species Named Entity Recognition.

Languages

The dataset is in English.

Dataset Structure

Data Instances

An example from the dataset is:

{'id': '2',
'tokens': ['Scp160p', 'is', 'a', '160', 'kDa', 'protein', 'in', 'the', 'yeast', 'Saccharomyces', 'cerevisiae', 'that', 'contains', '14', 'repeats', 'of', 'the', 'hnRNP', 'K', '-', 'homology', '(', 'KH', ')', 'domain', ',', 'and', 'demonstrates', 'significant', 'sequence', 'homology', 'to', 'a', 'family', 'of', 'proteins', 'collectively', 'known', 'as', 'vigilins', '.'],
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}

Data Fields

• id: Sentence identifier.
• tokens: Array of tokens composing a sentence.
• ner_tags: Array of tags, where 0 indicates no species mentioned, 1 signals the first token of a species and 2 the subsequent tokens of the species.

Data Splits

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

This version of the dataset is licensed under Creative Commons Attribution 4.0 International.

Citation Information

@article{crichton2017neural,
  title={A neural network multi-task learning approach to biomedical named entity recognition},
  author={Crichton, Gamal and Pyysalo, Sampo and Chiu, Billy and Korhonen, Anna},
  journal={BMC Bioinformatics},
  volume={18},
  number={1},
  pages={368},
  year={2017},
  publisher={BioMed Central}
  doi = {10.1186/s12859-017-1776-8},
  issn = {1471-2105},
  url = {https://doi.org/10.1186/s12859-017-1776-8},
}
@article{Gerner2010,
  author = {Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
  doi = {10.1186/1471-2105-11-85},
  issn = {1471-2105},
  journal = {BMC Bioinformatics},
  number = {1},
  pages = {85},
  title = {{LINNAEUS: A species name identification system for biomedical literature}},
  url = {https://doi.org/10.1186/1471-2105-11-85},
  volume = {11},
  year = {2010}
}

Contributions

Thanks to @edugp for adding this dataset.