An entity prototype-based multi-feature fusion network for named entity recognition

doi:10.1016/j.engappai.2026.115800

2026-07-31

PRE

Dan Wang

Jianchao Yao

Bin Song *

DOI：10.1016/j.engappai.2026.115800

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摘要

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• 实体原型网络在独立训练时能够高效聚合跨文档信息。 • 仅使用原型表示网络会导致跨文档特征提取中出现噪声干扰。 • 引入监督聚类算法显著增强了实体原型网络的特征聚合能力。 • 多特征融合网络中的自适应选择矩阵能有效融合不同网络架构的特征。 • 结合实体原型网络和多特征融合网络显著提升了跨文档实体分类性能。

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Engineering Applications of Artificial Intelligence

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xidian university

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