基于异构图和语义融合的实体关系抽取  

Entity-relation extraction based on heterogeneous graphs and semantic fusion

作　　者：唐贤伦[1] 丁河长 唐瑜泽 谢涛[1] 罗洪平[1] TANG Xianlun;DING Hechang;TANG Yuze;XIE Tao;LUO Hongping(School of Automation,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)

机构地区：[1]重庆邮电大学自动化学院,重庆400065

出　　处：《实验技术与管理》2024年第8期22-29,共8页Experimental Technology and Management

基　　金：重庆市研究生教育教学改革研究重大项目(yjg241008);重庆市自然科学基金面上项目(CSTB2022NSCQ-MSX0380)。

摘　　要：关系抽取是信息抽取中的一项重要任务,其目的是从非结构化文本中抽取出所有关系三元组。然而,如何有效地处理这一问题仍然是一个挑战,特别是对于关系重叠问题。为了有效处理重叠问题,该文提出一种基于异构图和语义融合的实体关系抽取方法:使用异构图将关系信息作为先验知识融入词表示,增强词表示的表示能力,使得模型能有效地处理单词实体重叠问题;使用语义融合模块将不同层次特征融合在一起作为关系分类模型的输入,使得模型能够有效地处理实体对重叠问题。所提方法在NYT和WebNLG数据集上取得了最好的效果,详细的实验也表明所提方法可以处理复杂的场景。[Objective]Relational extraction,which involves the extraction of all relational triples from unstructured text,is an important task in natural language processing.However,effectively addressing the problem of overlapping entity relations remains a challenge.Entity-relation overlap is a significant challenge in entity-relation extraction within natural language processing.Entity-relation overlap refers to the phenomenon in which an entity may have relationships with more than one entity or where multiple relationships exist between pairs of entities.[Methods]To better address the issue of relation overlap,this study proposes an entity-relation extraction method based on heterogeneous graphs and semantic fusion.The overall strategy is to first extract entities and then classify different pairs of entities into specific relationships.This approach effectively addresses the problem of single-entity overlap.To maximize entity extraction,heterogeneous layers are used to integrate predefined relationships as relational prior information into word representation.This enhances representation capability,making it more conducive to entity annotation tasks and reducing the extraction of redundant entities.After the entities are obtained,a global association matrix is employed to filter out entity pairs that do not have relational connections,thereby ensuring that only the correct entity pairs are selected.To better classify the relationship types between entity pairs with relational connections,a semantic fusion module is used to aggregate features at different levels as the input for the relational classification module.This can improve the performance of relational classification and address the problem of entity-pair overlap.[Results]Experimental results demonstrate that the proposed models outperform other benchmark models on the NYT and WebNLG datasets.Specifically,for the NYT data set,the proposed method improves the F1 value by 0.3%compared with the best existing method,and for the WebNLG data set,it improves the F1

关 键 词：实体关系抽取 异构图 语义融合 关系重叠 实体关系三元组 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]