  Entity linking is the task of linking mentions of named entities in natural
language text, to entities in a curated knowledge-base. This is of significant
importance in the biomedical domain, where it could be used to semantically
annotate a large volume of clinical records and biomedical literature, to
standardized concepts described in an ontology such as Unified Medical Language
System (UMLS). We observe that with precise type information, entity
disambiguation becomes a straightforward task. However, fine-grained type
information is usually not available in biomedical domain. Thus, we propose
LATTE, a LATent Type Entity Linking model, that improves entity linking by
modeling the latent fine-grained type information about mentions and entities.
Unlike previous methods that perform entity linking directly between the
mentions and the entities, LATTE jointly does entity disambiguation, and latent
fine-grained type learning, without direct supervision. We evaluate our model
on two biomedical datasets: MedMentions, a large scale public dataset annotated
with UMLS concepts, and a de-identified corpus of dictated doctor's notes that
has been annotated with ICD concepts. Extensive experimental evaluation shows
our model achieves significant performance improvements over several
state-of-the-art techniques.
