利用注意力-卷积混合网络和证据提取加强文档级关系提取

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Feiyu Zhang, Ruiming Hu, Guiduo Duan, Tianxi Huang
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引用次数: 0

摘要

文档级关系提取旨在提取文档中实体之间的关系。与句子级对应关系不同,文档级关系提取需要对多个句子进行推理,以提取复杂的关系三元组。最近的研究发现,增加额外的证据提取任务和使用提取的证据句子帮助预测可以提高文档级关系提取任务的性能,但是,这些方法仍然面临实体对之间的交互建模不足的问题。本文在回顾人类认知过程的基础上,提出了一种应用于实体对特征矩阵的混合网络 HIMAC,其中多头注意力子模块可以在特定推理路径上融合全局实体对信息,而卷积子模块则能够获取相邻实体对的局部信息。然后,我们将实体对的上下文交互信息纳入关系预测和证据提取任务中。最后,我们利用提取的证据句来进一步修正关系提取结果。我们在两个文档级关系提取基准数据集(DocRED/Re-DocRED)上进行了广泛的实验,实验结果表明我们的方法达到了最先进的性能(62.84/75.89 F1)。实验证明了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing Document-Level Relation Extraction with Attention-Convolutional Hybrid Networks and Evidence Extraction

Enhancing Document-Level Relation Extraction with Attention-Convolutional Hybrid Networks and Evidence Extraction

Document-level relation extraction aims at extracting relations between entities in a document. In contrast to sentence-level correspondences, document-level relation extraction requires reasoning over multiple sentences to extract complex relational triples. Recent work has found that adding additional evidence extraction tasks and using the extracted evidence sentences to help predict can improve the performance of document-level relation extraction tasks, however, these approaches still face the problem of inadequate modeling of the interactions between entity pairs. In this paper, based on the review of human cognitive processes, we propose a hybrid network HIMAC applied to the entity pair feature matrix, in which the multi-head attention sub-module can fuse global entity-pair information on a specific inference path, while the convolution sub-module is able to obtain local information of adjacent entity pairs. Then we incorporate the contextual interaction information learned by the entity pairs into the relation prediction and evidence extraction tasks. Finally, the extracted evidence sentences are used to further correct the relation extraction results. We conduct extensive experiments on two document-level relation extraction benchmark datasets (DocRED/Re-DocRED), and the experimental results demonstrate that our method achieves state-of-the-art performance (62.84/75.89 F1). Experiments demonstrate the effectiveness of the proposed method.

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来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
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