Qinghui Zhang, Yaya Sun, Pengtao Lv, Lei Lu, Mengya Zhang, Jinhui Wang, Chenxia Wan, Jingping Wang
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引用次数: 0
摘要
非结构化中文医学文本是实体和关系信息的丰富来源。从医学文本中提取实体关系对于构建医学知识图谱和帮助医护人员迅速做出明智决策至关重要。然而,从这些文本中提取实体关系是一项艰巨的挑战,特别是由于实体关系重叠的问题。本研究介绍了一种新颖的提取模型,该模型利用 RoFormer 的旋转位置编码(RoPE)技术有效地实现了相对位置编码。这种方法不仅能优化位置信息的利用,还能通过构建加权邻接矩阵捕捉句法依赖信息。在特征融合阶段,该模型采用了实体关注机制对特征进行深度融合,从而有效地解决了实体关系重叠的难题。实验结果表明,我们的模型在具有重叠实体关系的数据集上取得了 83.42 的 F1 分数,明显优于其他基线模型。
RoGraphER: Enhanced Extraction of Chinese Medical Entity Relationships Using RoFormer Pre-Trained Model and Weighted Graph Convolution
Unstructured Chinese medical texts are rich sources of entity and relational information. The extraction of entity relationships from medical texts is pivotal for the construction of medical knowledge graphs and aiding healthcare professionals in making swift and informed decisions. However, the extraction of entity relationships from these texts presents a formidable challenge, notably due to the issue of overlapping entity relationships. This study introduces a novel extraction model that leverages RoFormer’s rotational position encoding (RoPE) technique for an efficient implementation of relative position encoding. This approach not only optimizes positional information utilization but also captures syntactic dependency information by constructing a weighted adjacency matrix. During the feature fusion phase, the model employs an entity attention mechanism for a deeper integration of features, effectively addressing the challenge of overlapping entity relationships. Experimental outcomes demonstrate that our model achieves an F1 score of 83.42 on datasets featuring overlapping entity relations, significantly outperforming other baseline models.