吉林大学学报（理学版）2025，Vol.63Issue(2)：428-436,9.DOI:10.13413/j.cnki.jdxblxb.2023508
基于实体类别信息的数据分析与关系抽取模型构建
Data Analysis and Relation Extraction Model Construction Based on Entity Category Information
摘要
Abstract
Aiming at the problem of multiple mentions of entities and the noise of entity pairs in the document-level relation extraction task,we proposed a relation extraction model(EUT model)based on entity type information.The model improved the relation extraction results through two sub-tasks:entity type judgment and a priori of the relation types produced by the type pairs.After the entity type judgment task labelled entities by type,then categorized all mentions of the entity by type,so that multiple mentions of the entity produced richer and similar feature representations.The relation category prior task enabled the model to obtain a prior of the relation distribution generated by the head and tail types of entity pairs,and reduced erroneous entity pair noise through the categories of entity pairs.In order to verify the effectiveness of the EUT model,the experiments were conducted on two document-level datasets,DocRED and Re-DocRED.The experimental results show that the model effectively utilizes the entity type information and achieves better relation extraction results compared to the base model,indicating that entity type information has an important impact on document-level relation extraction.关键词
文档级关系抽取/知识图谱/结构化先验/自然语言处理Key words
document-level relation extraction/knowledge graph/structured prior/natural language processing分类
信息技术与安全科学引用本文复制引用
杨航,张啸成,张永刚..基于实体类别信息的数据分析与关系抽取模型构建[J].吉林大学学报（理学版）,2025,63(2):428-436,9.基金项目
吉林省自然科学基金(批准号:20200201447JC). （批准号:20200201447JC）
