.png){kind=icon}
返回
Segmentation-aware relational graph convolutional network with multi-layer CRF for nested named entity recognition
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-024-01551-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Named Entity Recognition (NER) is fundamental in natural language processing, involving identifying entity spans and types within a sentence. Nested NER contains other entities, which pose a significant challenge, especially pronounced in the domain of medical-named entities due to intricate nesting patterns inherent in medical terminology. Existing studies can not capture interdependencies among different entity categories, resulting in inadequate performance in nested NER tasks. To address this problem, we propose a novel Layer-based architecture with Segmentation-aware Relational Graph Convolutional Network (LSRGCN) for Nested NER in the medical domain. LSRGCN comprises two key modules: a shared segmentation-aware encoder and a multi-layer conditional random field decoder. The former part provides token representation including boundary information from sentence segmentation. The latter part can learn the connections between different entity classes and improve recognition accuracy through secondary decoding. We conduct experiments on four datasets. Experimental results demonstrate the effectiveness of our model. Additionally, extensive studies are conducted to enhance our understanding of the model and its capabilities.
Keyword:
Named entity recognition
Shared segmentation-aware encoder
Relational graph convolutional network
Multi-layer conditional random field
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
机构
引用论文
.png){kind=icon}
分享收藏Sodium channel kinetic changes that produce Brugada syndrome or progressive cardiac conduction system disease产生Brugada综合征或进行性心脏传导系统疾病的钠通道动力学变化
PREAI
分享收藏分享收藏
查看更多内容