Biomedical entity linking is the task of identifying mentions of biomedical
concepts in text documents and mapping them to canonical entities in a target
thesaurus. Recent advancements in entity linking using BERT-based models follow
a retrieve and rerank paradigm, where the candidate entities are first selected
using a retriever model, and then the retrieved candidates are ranked by a
reranker model. While this paradigm produces state-of-the-art results, they are
slow both at training and test time as they can process only one mention at a
time. To mitigate these issues, we propose a BERT-based dual encoder model that
resolves multiple mentions in a document in one shot. We show that our proposed
model is multiple times faster than existing BERT-based models while being
competitive in accuracy for biomedical entity linking. Additionally, we modify
our dual encoder model for end-to-end biomedical entity linking that performs
both mention span detection and entity disambiguation and out-performs two
recently proposed models.

本研究提出了一种基于 BERT 的双编码器模型，可一次处理文档中的多个关键词，解决了现有模型效率低的问题，在生物医学实体链接领域取得了与现有模型相当的准确性，并对其进行了改进，实现了端到端生物医学实体链接，表现出更好的性能.

使用双编码器快速有效的生物医学实体链接

Fast and Effective Biomedical Entity Linking Using a Dual Encoder

We show that it is feasible to perform entity linking by training a dual
encoder (two-tower) model that encodes mentions and entities in the same dense
vector space, where candidate entities are retrieved by approximate nearest
neighbor search. Unlike prior work, this setup does not rely on an alias table
followed by a re-ranker, and is thus the first fully learned entity retrieval
model. We show that our dual encoder, trained using only anchor-text links in
Wikipedia, outperforms discrete alias table and BM25 baselines, and is
competitive with the best comparable results on the standard TACKBP-2010
dataset. In addition, it can retrieve candidates extremely fast, and
generalizes well to a new dataset derived from Wikinews. On the modeling side,
we demonstrate the dramatic value of an unsupervised negative mining algorithm
for this task.

使用双编码器模型，在相同的密集向量空间中对实体和提及进行编码，并通过近似最近邻搜索检索候选实体，从而实现实体链接。通过使用维基百科中的锚文本链接训练双编码器，该方法优于离散别名表和 BM25 基线，并且与标准 TACKBP-2010 数据集中最佳结果相当，还可以快速检索候选者，并可以很好地推广到从 Wikinews 推导出的新数据集。在建模方面，我们演示了无监督的负采矿算法在这一任务中的重要作用。