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.
The paper introduces a new gold-standard corpus corpus of biomedical scientific literature manually annotated with UMLS concept mentions. Details the construction of a manually annotated dataset covering biomedical concepts that is larger and covered by a larger ontology than previous datasets. This paper uses MedMentions, a TaggerOne semi-Markov model for end-to-end concept recognition and linking on a set of Pubmed abstracts to label papers with biomedical concepts/entities