DRE-3DC: 基于三维表征建模的篇章级关系抽取模型

王宇; 王震; 温立强; 李伟平; 赵文

doi:10.12263/DZXB.20221187

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DRE-3DC: 基于三维表征建模的篇章级关系抽取模型

学术论文 | 更新时间：2025-12-08

- DRE-3DC: 基于三维表征建模的篇章级关系抽取模型
- DRE-3DC: Document-Level Relation Extraction with Three-Dimensional Representation Combination Modeling
- 电子学报 2024年52卷第9期页码：2950-2960
- 作者机构：
  
  1.北京大学软件与微电子学院，北京 100871
  2.北京大学软件工程国家工程研究中心，北京100871
- 作者简介：
  
  [ "王宇男,1978年出生,辽宁沈阳人.北京大学软件与微电子学院2020级博士研究生,主要研究方向为知识图谱构建和自然语言处理.E-mail: wangyu_cn@stu.pku.edu.cn" ]
  [ "王震男，1994年出生，山东济南人.北京大学软件与微电子学院博士研究生，主要研究方向为知识图谱构建和自然语言处理.E-mail: wangzhen@stu.pku.edu.cn" ]
  [ "温立强男，1991年出生，山西吕梁人.北京大学软件与微电子学院博士研究生，主要研究方向为知识图谱问答和智能检索.E-mail: wenlq@pku.edu.cn" ]
  [ "李伟平男，1973年3月，辽宁凌源人.北京大学教授、博士生导师，主要研究方向为大数据分析和信息抽取.E-mail: wpli@ss.pku.edu.cn" ]
  [ "赵文男，1967年出生，辽宁大连人.现为北京大学软件工程国家工程研究中心研究员、博士生导师，主要研究领域为知识图谱、软件工程和软件安全. E-mail: zhaowen@pku.edu.cn" ]
- 基金信息：
  
  国家重点研发计划(2020YFC0833300)
- DOI：10.12263/DZXB.20221187
  中图分类号： TP391.1
- 收稿：2022-10-20，
  
  修回：2023-02-11，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
王宇, 王震, 温立强, 等. DRE-3DC: 基于三维表征建模的篇章级关系抽取模型[J]. 电子学报, 2024, 52(09): 2950-2960.

WANG Yu, WANG Zhen, WEN Li-qiang, et al. DRE-3DC: Document-Level Relation Extraction with Three-Dimensional Representation Combination Modeling[J]. Acta Electronica Sinica, 2024, 52(09): 2950-2960.
王宇, 王震, 温立强, 等. DRE-3DC: 基于三维表征建模的篇章级关系抽取模型[J]. 电子学报, 2024, 52(09): 2950-2960. DOI：10.12263/DZXB.20221187

WANG Yu, WANG Zhen, WEN Li-qiang, et al. DRE-3DC: Document-Level Relation Extraction with Three-Dimensional Representation Combination Modeling[J]. Acta Electronica Sinica, 2024, 52(09): 2950-2960. DOI：10.12263/DZXB.20221187

摘要

篇章级关系抽取任务旨在从非结构化文档的多个句子中提取事实，是构建领域知识库和知识问答应用的关键环节，相较于句子级关系抽取，该任务既要求模型能够基于文档结构特征捕获实体间的复杂交互，还要应对严重的关系类别长尾分布问题.现有基于表格的关系抽取模型主要对文档进行“实体/实体”二维建模，采用多层卷积网络或局部注意力机制提取实体间的交互特征，由于未显式对关系语义进行解耦建模，使得模型无法避免类别重叠影响和捕获关系的方向性特征，导致缺乏实体交互的充分语义信息.针对上述挑战，本文提出了一种基于三维表征建模的篇章级关系抽取模型DRE-3DC（Document-Level Relation Extraction with Three-Dimensional Representation Combination Modeling），对二维表格建模方式进行扩展，形成“实体/实体/关系”三维表征建模，采用基于形变卷积的三重注意力机制有效区分和聚合不同语义空间下的实体间及实体与关系的交互表征，自适应地增强模型对文档结构特征的聚合.同时，采用多任务学习方法增强模型对文档整体关系类别组合的感知来缓解篇章级关系抽取任务中的关系类别长尾分布问题.在DocRED和Revisit-DocRED两个篇章级关系抽取数据集上进行的实验结果表明，DRE-3DC模型性能良好，并通过消融实验、对比分析和实例分析，验证了本文所提方法的有效性.

Abstract

The task of document-level relation extraction aims to extract facts from multiple sentences of unstructured documents

which is a key step in the construction of domain knowledge graph and knowledge answering application. The task requires that the model not only capture the complex interactions between entities based on the structural features of documents

but also deal with the serious long-tail category distribution problem. Existing table-based relation extraction models try to solve this issue

but they mainly model documents in two-dimensional “entity/entity” space

and use multi-layer convolutional network or restricted self-attention mechanism to extract the interaction features between entities

which cannot avoid the influence of category overlap and capture the directional features of relationships

resulting in the lack of decoupled semantic information of interaction. For the above challenges

this paper proposes a new document-level relation extraction model

named DRE-3DC (Document-Level Relation Extraction with Three-Dimensional Representation Combination Modeling)

in which the “entity/entity” modeling extend to the form of three-dimensional “entity/entities/relationship” modeling method. Based on the deformable convolution in triple attention mechanism

the model effectively distinguishes and integrates the interaction features under different semantic space and adaptively captures the document structural features. At the same time

we propose a multi-task learning method to enhance the perception of relation category combination of documents to alleviate the long-tail distribution problem. The experimental results reveal better score on DocRED and Revisit-DocRED dataset respectively. The effectiveness of the proposed method was verified by ablation experiment

comparative analysis and example analysis.

关键词

Keywords

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