融合关系传递信息的双图文档级关系抽取方法

Method of Document Level Relation Extraction Based on Fusion of Relational Transfer Information Using Double Graph

文档级关系抽取指在长段落的非结构性文本中抽取实体以及实体之间的关系。相较于传统的句子级关系抽取,文档级关系抽取需要融合多个句子的上下文信息,并且加以逻辑推理,才能抽取出关系三元组。针对目前文档级关系抽取方法中存在的文档语义信息建模不够完整且抽取效果具有局限性等问题,提出了一种融合关系传递信息的双图文档级关系抽取方法。利用关系信息传递性将不同句子中提及之间的交互信息引入路径构造中,加以使用同句子中提及的交互信息以及提及之间的共指信息,构建提及节点间的路径集合,提高文档建模的完整性;应用路径集合和提及节点搭建提及层次的图聚合网络,建立文档语义信息模型;经过图卷积网络的信息迭代后,将相同实体的不同提及节点的信息进行融合,形成实体节点,构成实体层次的图推理网络;最终根据实体图节点间的路径信息进行逻辑推理,抽取出实体间的关系。在公开数据集DocRED上的实验结果表明,相对于基准模型,所提模型的F1值有1.2的提升,证明了该方法的有效性。

Document-level relation extraction refers to the extraction of entities and their relations from long paragraphs of unstructured text.Compared to traditional sentence-level relation extraction,document-level relation extraction requires the integration of contextual information from multiple sentences and logical reasoning to extract relation triples.In response to the current limitations of document-level relation extraction methods,such as incomplete modeling of document semantic information and li-mited extraction effects,a double-graph document-level relation extraction method that integrates relational transfer information is proposed.Interactions mentioned between different sentences are introduced into the path construction through the transitivity of relational information,and the interaction information mentioned in the same sentence as well as the coreference information between mentions are used to construct the path set between mention nodes,so as to improve the completeness of document modeling.A mention hierarchy graph aggregation network is constructed using the path set and mention nodes,and a document semantic information model is established.After the information iteration of the graph convolutional network(GCN),the information of different mention nodes of the same entity is fused to form entity node,which constitutes an entity-level graph reasoning network.Finally,logical inference is performed based on the path information between entity graph nodes to extract the relation between entities.The proposed model is experimented on the public dataset DocRED(document-level relation extraction dataset),and the experimental results show a improvement of 1.2(F1)compared to the baseline model,which proves the effectiveness of the proposed method.