Datasets:
Aremaki
/

Languages:
English
ArXiv:
License:
Dataset Viewer

The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.

Dataset Card for MedMentions

MedMentions is a new manually annotated resource for the recognition of biomedical concepts. What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000 abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over 3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines.

Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected from among papers released on PubMed in 2016, that were in the biomedical field, published in the English language, and had both a Title and an Abstract.

Annotators: We recruited a team of professional annotators with rich experience in biomedical content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in these papers.

Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain insight on the annotation quality of MedMentions, we randomly selected eight papers from the annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not participate in the annotation task then each reviewed four papers. The agreement between Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%.

Citation Information

@misc{mohan2019medmentions,
      title={MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts},
      author={Sunil Mohan and Donghui Li},
      year={2019},
      eprint={1902.09476},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
Downloads last month
2

Collection including Aremaki/MedMentions

Paper for Aremaki/MedMentions