Academy of Military Medical Sciences, Beijing, 100850, China.
School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China.
J Biomed Inform. 2024 Sep;157:104719. doi: 10.1016/j.jbi.2024.104719. Epub 2024 Aug 29.
Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level relation extraction can capture the associations between different entities throughout the entire document, which is found to be more practical for biomedical text information. However, current biomedical extraction methods mainly concentrate on sentence-level relation extraction, making it difficult to access the rich structural information contained in documents in practical application scenarios. We put forward SSGU-CD, a combined Semantic and Structural information Graph U-shaped network for document-level Chemical-Disease interaction extraction. This framework effectively stores document semantic and structure information as graphs and can fuse the original context information of documents. Using the framework, we propose a balanced combination of cross-entropy loss function to facilitate collaborative optimization among models with the aim of enhancing the ability to extract Chemical-Disease interaction relations. We evaluated SSGU-CD on the document-level relation extraction dataset CDR and BioRED, and the results demonstrate that the framework can significantly improve the extraction performance.
针对跨多句话推断化学实体和疾病实体之间的相互作用关系，提出了一种针对化学-疾病文档级交互提取的方法。与句子级关系提取相比，文档级关系提取可以捕获整个文档中不同实体之间的关联，这对于生物医学文本信息来说更加实用。然而，当前的生物医学提取方法主要集中在句子级关系提取上，这使得在实际应用场景中难以访问文档中包含的丰富结构信息。我们提出了 SSGU-CD，一种用于文档级化学-疾病交互提取的语义和结构信息图 U 形网络。该框架有效地将文档语义和结构信息存储为图，并可以融合文档的原始上下文信息。使用该框架，我们提出了一种交叉熵损失函数的平衡组合，以促进模型之间的协同优化，从而提高提取化学-疾病交互关系的能力。我们在文档级关系提取数据集 CDR 和 BioRED 上对 SSGU-CD 进行了评估，结果表明该框架可以显著提高提取性能。
