2021年3月1日 · 本文介绍了一种将图卷积网络 (GCN)应用于命名实体识别 (NER)的新方法。 通过结合句法依存树信息，该方法在原有Bi-LSTM+CRF模型的基础上提升了2.2%的准确率。 此 …

2017年9月28日 · In this paper we investigate the role of the dependency tree in a named entity recognizer upon using a set of GCN. We perform a comparison among different NER …

--gcn=1 # gcn=0/gcn=1 bilstm输入到gcn/gcn=2 bilstm+gcn. 在原本BERT-BILSTM-CRF上融合GCN和词性标签等做NER任务. Contribute to ttttong/BERT-BILSTM-GCN-CRF-for-NER …

2023年3月17日 · The advancement of deep learning in graphs made it possible to exploit the dependencies in text using a Graph Convolution Network (GCN) on the dependency tree. This …

2022年9月21日 · 为了更好地建模上述两种外部匹配关系，我们提出了一个用于多模态NER任务的关系增强图卷积网络（R-GCN）。 具体来说，R-GCN主要包括两个模块：第一个模块构建了 …

This is an implementation of a named entity recognizer that uses Graph Convolutional Networks. The reference article is Graph Convolutional Networks for Named Entity Recognition. This …

2024年6月17日 · GCN-NER 是一个基于 Graph Convolutional Networks 的命名实体识别系统，它利用词向量和词性标注来增强双向LSTM的实体识别能力。 这个开源项目由 ContextScout 团 …

In this paper, we propose to leverage the external matching relations between different (text, image) pairs to improve the performance on the MNER task. We design a Relation-enhanced …

1 天前 · This paper proposes a novel approach that integrates Named Entity Recognition (NER) and LLM embeddings within a graph-based framework for document clustering. The method …

Incorporating the dependency information can be done with graph neural networks (GNNs) such as graph convolu-tional networks (GCNs) (Kipf and Welling, 2017). However, simply stacking …