基于图神经网络文档相似度的实体与关系层次匹配方法

A Hierarchical Matching Method of Entity and Relation Based on Graph Neural Network for Document Similarity

针对文本中深层语义难以计算的问题,提出了基于句法依存关系的多头图注意力实体关系联合抽取模型和融合层次类型的文档相似性匹配。首先通过多头图注意力网络对文本进行实体关系抽取,然后设计融合层次类型的词移距离相似性计算方法以及基于图相似的文档相似性计算模型,利用文档中的实体和关系构建图结构,根据图级特征进行相似性计算。最后,通过对比实验验证了所提方法在文档相似性计算、图相似度计算和图分类任务中的有效性。

Aiming at the difficulty to mine deep semantics in text,a multi-head graph attention entityrelation joint extraction model based on syntactic dependencies and a fusion hierarchical type of document similarity matching were proposed.Firstly,the entity relation extraction was carried out on the text through the multi-head graph attention network.Then,the word shift distance similarity calculation method of fusion hierarchical type and the document similarity calculation model based on graph similarity were designed,and the graph structure was constructed by using the entities and relations in the document.Thus,the features representing the graph level were obtained for similarity calculation.Finally,the effectiveness of the proposed method in document similarity calculation,graph similarity calculation and graph classification tasks was verified by comparative experiments.