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. One recognized challenge in developing automated biomedical entity extraction systems is the lack of richly annotated training datasets. While there are a few such datasets available, the annotated corpus often contains no more than a few thousand annotated entity mentions. Additionally, the annotated entities are limited to a few types of biomedical concepts such as diseases BID4 , gene ontology terms BID18 , or chemicals and diseases BID9 . Researchers targeting the recognition of multiple biomedical entity types have had to resort to specialized machine learning techniques for combining datasets labelled with subsets of the full target set, e.g. using multi-task learning BID3 , or a modified Conditional Random Field cost which allows un-labeled tokens to take any labels not in the current dataset's target set BID5 . To promote the development of state-of-the-art entity linkers targeting a more comprehensive coverage of biomedical concepts, we decided to create a large concept-mention annotated gold standard dataset named 'MedMentions' BID11 .With the release of MedMentions, we hope to address two key needs for developing better biomedical concept recognition systems: (i) a much broader coverage of the fields of biology and medicine through the use of the Unified Medical Language System (UMLS) as the target ontology, and (ii ) a significantly larger annotated corpus than available today, to meet the data demands of today's more complex machine learning models for concept recognition.The paper begins with an introduction to the MedMentions annotated corpus, including a subcorpus aimed at information retrieval systems. This is followed by a comparison with a few other large datasets annotated with biomedical entities. Finally , to promote further research on large ontology named entity recognition and linking, we present metrics for a baseline end-to-end concept recognition (entity type recognition and entity linking) model trained on the MedMentions corpus. We presented the formal release of a new resource, named MedMentions, for biomedical concept recognition, with a large manually annotated annotated corpus of over 4,000 abstracts targeting a very large fine-grained concept ontology consisting of over 3 million concepts. We also included in this release a targeted sub-corpus (MedMentions ST21pv), with standard training, development and test splits of the data, and the metrics of a baseline concept recognition model trained on this subset, to allow researchers to compare the metrics of their concept recognition models.
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