融合预训练语言模型和标签依赖知识的关系抽取方法

关系抽取旨在从未经标注的自由文本中抽取实体间的关系。然而,现有的方法大都孤立地预测每一个关系而未考虑关系标签相互之间的丰富语义关联。该文提出了一种融合预训练语言模型和标签依赖知识的关系抽取模型。该模型通过预训练模型BERT编码得到句子和两个目标实体的语义信息,使用图卷积网络建模关系标签之间的依赖图,并结合上述信息指导最终的关系分类。实验结果显示,该文方法性能相较于基线方法得到了显著提高。

Combination of Pre-trained Language Model and Label Dependency for Relation Extraction

Relation extraction aims to extract the relations between entities from unlabeled free text. This paper proposes a relation extraction model that combines the pre-trained language model and label dependency knowledge. Specifically, given a sentence as the input, we first generate a deep contextualized word representation for the sentence and the two target entities using a pre-trained BERT encoder. At the same time, a multi-layer graph convolutional network is applied to model the dependency graph between the relation labels. Finally, we combine the above information to guide the relation classification. The experimental results show that our approach significantly outperforms the baselines.