Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization

• URL: http://arxiv.org/abs/2308.12025v1
• Date: Wed, 23 Aug 2023 09:32:40 GMT
• Title: Knowledge-injected Prompt Learning for Chinese Biomedical Entity Normalization
• Authors: Songhua Yang, Chenghao Zhang, Hongfei Xu and Yuxiang Jia
• Abstract summary: We propose a novel Knowledge-injected Prompt Learning (PL-Knowledge) method to tackle the Biomedical Entity Normalization (BEN) task. 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, the additional knowledge enhances the model's ability to capture latent relationships between medical entities.
• Score: 6.927883826415262
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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