用语义索引+生成模型，让医学概念识别更准更快
MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition
- 将概念识别转为索引查找，解决术语歧义问题
- 在3个数据集上实现隐含概念识别，无需标注输入
- 适合医学专家使用，低资源下仍有效
从文本中识别生物医学概念对本体优化、知识图谱构建和概念关系发现至关重要。传统方法依赖显式提及识别，难以捕捉未明确表述的复杂概念。为此，我们提出MA-COIR框架，将概念识别重构为索引-识别任务。通过为概念分配语义搜索索引（ssIDs），该框架解决了本体条目中的歧义问题并提升了识别效率。采用基于预训练BART模型并在小数据集上微调的方法，显著降低计算开销，便于领域专家使用。此外，结合大语言模型生成查询与合成数据，进一步提升低资源场景下的识别性能。在CDR、HPO和HOIP三个场景的实验表明，MA-COIR无需推理时进行提及级标注，即可有效识别显性和隐性概念，推动生物医学领域本体驱动的概念识别发展。代码与构建数据已公开于https://github.com/sl-633/macoir-master。
原文摘要 · Abstract (English)
Recognizing biomedical concepts in the text is vital for ontology refinement, knowledge graph construction, and concept relationship discovery. However, traditional concept recognition methods, relying on explicit mention identification, often fail to capture complex concepts not explicitly stated in the text. To overcome this limitation, we introduce MA-COIR, a framework that reformulates concept recognition as an indexing-recognition task. By assigning semantic search indexes (ssIDs) to concepts, MA-COIR resolves ambiguities in ontology entries and enhances recognition efficiency. Using a pretrained BART-based model fine-tuned on small datasets, our approach reduces computational requirements to facilitate adoption by domain experts. Furthermore, we incorporate large language models (LLMs)-generated queries and synthetic data to improve recognition in low-resource settings. Experimental results on three scenarios (CDR, HPO, and HOIP) highlight the effectiveness of MA-COIR in recognizing both explicit and implicit concepts without the need for mention-level annotations during inference, advancing ontology-driven concept recognition in biomedical domain applications. Our code and constructed data are available at https://github.com/sl-633/macoir-master.
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