MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts
This provides a large-scale resource for researchers in biomedical NLP, addressing the need for annotated data in named entity recognition and linking, though it is incremental as it builds on existing annotation efforts.
The authors introduced MedMentions, a manually annotated biomedical corpus with over 4,000 abstracts and 350,000 linked mentions, using a large ontology of over 3 million UMLS concepts, to support research in entity recognition and linking.
This paper presents the formal release of 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.