Anatomical entity mention recognition

This is the home page for AnatomyTagger, an open-source entity mention tagger for anatomical entities, the AnatEM anatomical entity mention corpus, and related open data resources presented in

if you use any of the tools and resources available from this page, please cite this paper.


The following tools and resources are provided:

  • AnatEM corpus: 1212 documents manually annotated for anatomical entity mentions
  • AnatomyTagger: Python implementation of anatomical entity mention tagger

AnatEM corpus

example annotations

The extended Anatomical Entity Mention corpus (AnatEM) consists of 1212 documents (approx. 250,000 words) manually annotated to identify over 13,000 mentions of anatomical entities. Each annotation is assigned one of 12 granularity-based types such as Cellular component, Tissue and Organ, defined with reference to the Common Anatomy Reference Ontology. The corpus builds in part on two previously introduced resources, AnEM and MLEE. The corpus annotations were created using the brat annotation tool.


The corpus distribution contains the annotations in the standoff format used by the brat tool, the column-based CoNLL format, and the NERsuite format.


AnatomyTagger is a Python-based entity mention tagger implemented using the NERsuite named entity recognition toolkit. The tagger is provided with various lexical and corpus resources for anatomical entity tagging and is simple to train and to apply.



These tools and resources were introduced in

Please cite this paper if you use any of the tools and resources available from this page.