Knowledge-injected Prompt Learning for Chinese Biomedical Entity
  Normalization
Songhua Yang Chenghao Zhang Hongfei Xu Yuxiang Jia
The Biomedical Entity Normalization (BEN) task aims to align raw,
unstructured medical entities to standard entities, thus promoting data
coherence and facilitating better downstream medical applications. Recently,
prompt learning methods have shown promising results in this task. However,
existing research falls short in tackling the more complex Chinese BEN task,
especially in the few-shot scenario with limited medical data, and the vast
potential of the external medical knowledge base has yet to be fully harnessed.
To address these challenges, we propose a novel Knowledge-injected Prompt
Learning (PL-Knowledge) method. Specifically, our approach consists of five
stages: candidate entity matching, knowledge extraction, knowledge encoding,
knowledge injection, and prediction output. By effectively encoding the
knowledge items contained in medical entities and incorporating them into our
tailor-made knowledge-injected templates, the additional knowledge enhances the
model's ability to capture latent relationships between medical entities, thus
achieving a better match with the standard entities. We extensively evaluate
our model on a benchmark dataset in both few-shot and full-scale scenarios. Our
method outperforms existing baselines, with an average accuracy boost of
12.96\% in few-shot and 0.94\% in full-data cases, showcasing its excellence in
the BEN task.