  Biomedical entity linking aims to map biomedical mentions, such as diseases
and drugs, to standard entities in a given knowledge base. The specific
challenge in this context is that the same biomedical entity can have a wide
range of names, including synonyms, morphological variations, and names with
different word orderings. Recently, BERT-based methods have advanced the
state-of-the-art by allowing for rich representations of word sequences.
However, they often have hundreds of millions of parameters and require heavy
computing resources, which limits their applications in resource-limited
scenarios. Here, we propose a lightweight neural method for biomedical entity
linking, which needs just a fraction of the parameters of a BERT model and much
less computing resources. Our method uses a simple alignment layer with
attention mechanisms to capture the variations between mention and entity
names. Yet, we show that our model is competitive with previous work on
standard evaluation benchmarks.
