基于双向译码的实体关系抽取方法

doi:10.3969/j.issn.0255-8297.2025.03.010

摘要/Abstract

摘要： 针对现有关系三元组提取方法存在的误差传播、嵌套三元组难提取和主客体难对齐等问题，从实体抽取、主客体对齐和关系判断3个子任务的全新视角出发，提出了一种新的基于联合实体关系抽取框架的双向译码解码模型。其中，双向抽取框架极大地减少了级联误差传播；基于注意力机制的译码解码方法能够有效处理常规方法嵌套三元组难提取的问题，并有效对齐了主体和客体；关系判断模块的二部图方法充分挖掘了实体对之间的关系，实现了准确高效的关系判断。公开数据集上的实验结果验证了所提模型的性能。

关键词: 自然语言处理, 信息提取, 实体抽取, 关系抽取, 误差传播, 三元组

Abstract: To address the challenges of error propagation, overlapping triple problem and subject-object alignment in the existing relationship triplet extraction methods, this study proposes a novel bidirectional translation and decoding model. The model reframes the extraction process into three sub-tasks: entity extraction, subject-object alignment and relationship judgment. The bidirectional structure effectively alleviates error propagation, while the translation and decoding method based on the attention mechanism deals with the overlapping triple problem and aligns the subject and object. Finally, a bipartite entity-torelationship diagram fully explores the relationship between entity pairs, enabling accurate relationship judgment. Experimental results on public datasets have validated the performance of the proposed model.

Key words: nature language processing, information extraction, entity extraction, relationship extraction, error propagation, triplet

中图分类号:

TP391.1
TP18

刘辉, 张智, 陈宇鹏. 基于双向译码的实体关系抽取方法[J]. 应用科学学报, 2025, 43(3): 491-503.

LIU Hui, ZHANG Zhi, CHEN Yupeng. Entity Relationship Extraction Method Based on Bidirectional Decoding[J]. Journal of Applied Sciences, 2025, 43(3): 491-503.

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