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.

本研究提出了一种轻量级神经方法，利用对齐层和注意力机制来捕捉提到与实体的不同名称变化，以解决生物医学概念链接中同一实体具有各种名称变体的挑战，并且在标准评估基准上证明该模型表现竞争力。