论文标题
DG-Spanbert有效的长距离关系提取
Efficient long-distance relation extraction with DG-SpanBERT
论文作者
论文摘要
在自然语言处理中，关系提取旨在合理理解非结构化的文本。在这里，我们提出了一个基于Spanbert的新型图形卷积网络（DG-Spanbert），该网络使用预训练的语言模型Spanbert和图形卷积网络从原始句子中提取语义特征，以填充潜在特征。我们的DG-Spanbert模型继承了Spanbert在从大规模语料库中学习丰富的词汇特征方面的优势。由于GCN在依赖树上的使用，它还具有捕获实体之间远程关系的能力。实验结果表明，我们的模型表现优于其他基于依赖关系的模型，并在Tacred数据集中实现了最先进的性能。
In natural language processing, relation extraction seeks to rationally understand unstructured text. Here, we propose a novel SpanBERT-based graph convolutional network (DG-SpanBERT) that extracts semantic features from a raw sentence using the pre-trained language model SpanBERT and a graph convolutional network to pool latent features. Our DG-SpanBERT model inherits the advantage of SpanBERT on learning rich lexical features from large-scale corpus. It also has the ability to capture long-range relations between entities due to the usage of GCN on dependency tree. The experimental results show that our model outperforms other existing dependency-based and sequence-based models and achieves a state-of-the-art performance on the TACRED dataset.
