Dual-stream dynamic graph structure network for document-level relation extraction

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yu Zhong, Bo Shen
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引用次数: 0

Abstract

Extracting structured information from unstructured text is crucial for knowledge management and utilization, which is the goal of document-level relation extraction. Existing graph-based methods face issues with information confusion and integration, limiting the reasoning capabilities of the model. To tackle this problem, a dual-stream dynamic graph structural network is proposed to model documents from various perspectives. Leveraging the richness of document information, a static document heterogeneous graph is constructed. A dynamic heterogeneous document graph is then induced based on this foundation to facilitate global information aggregation for entity representation learning. Additionally, the static document graph is decomposed into multi-level static semantic graphs, and multi-layer dynamic semantic graphs are further induced, explicitly segregating information from different levels. Information from different streams is effectively integrated via an information integrator. To mitigate the interference of noise during the reasoning process, a noise regularization mechanism is also designed. The experimental results on three extensively utilized publicly accessible datasets for document-level relation extraction demonstrate that our model achieves F1 scores of 62.56%, 71.1%, and 86.9% on the DocRED, CDR, and GDA datasets, respectively, significantly outperforming the baselines. Further analysis also demonstrates the effectiveness of the model in multi-entity scenarios.
用于文档级关系提取的双流动态图结构网络
从非结构化文本中提取结构化信息对于知识管理和利用至关重要,这也是文档级关系提取的目标。现有的基于图的方法面临着信息混淆和整合的问题,限制了模型的推理能力。为解决这一问题,我们提出了一种双流动态图结构网络,从不同角度对文档进行建模。利用丰富的文档信息,构建静态文档异构图。然后在此基础上诱导出动态异构文档图,以促进实体表征学习的全局信息聚合。此外,静态文档图被分解成多层次的静态语义图,并进一步诱导出多层次的动态语义图,明确分离来自不同层次的信息。来自不同信息流的信息通过信息集成器进行有效集成。为了减少推理过程中的噪声干扰,还设计了噪声正则化机制。在三个广泛使用的公开文档级关系提取数据集上的实验结果表明,我们的模型在 DocRED、CDR 和 GDA 数据集上的 F1 分数分别达到了 62.56%、71.1% 和 86.9%,明显优于基线模型。进一步的分析还证明了该模型在多实体场景中的有效性。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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