MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts
doi:10.24432/C5G59C
TL;DR
The paper introduces a new gold-standard corpus corpus of biomedical scientific literature manually annotated with UMLS concept mentions. This paper presents the formal release of {\em MedMentions}, 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. In addition to the full corpus, a sub-corpus of MedMentions is also presented, comprising annotations for a subset of UMLS 2017 targeted towards document retrieval. To encourage research in Biomedical Named Entity Recognition and Linking, data splits for training and testing are included in the release, and a baseline model and its metrics for entity linking are also described.
Citation
@inproceedings{
mohan2019medmentions,
title={MedMentions: A Large Biomedical Corpus Annotated with {\{}UMLS{\}} Concepts},
author={Sunil Mohan and Donghui Li},
booktitle={Automated Knowledge Base Construction (AKBC)},
year={2019},
url={https://openreview.net/forum?id=SylxCx5pTQ},
doi={10.24432/C5G59C}
}
