Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization
This work addresses the problem of aligning unstructured Chinese medical entities to standard ones for improved data coherence in biomedical applications, representing an incremental advance by integrating external knowledge into prompt learning.
The paper tackles the Chinese Biomedical Entity Normalization task by proposing a knowledge-injected prompt learning method, achieving an average accuracy boost of 12.96% in few-shot and 0.94% in full-data scenarios compared to baselines.
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.