注意力图卷积网络关系提取模型的多重依赖性表示法

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-10-04 DOI:10.1049/cps2.12080
Zhao Liangfu, Xiong Yujie, Gao Yongbin, Yu Wenjun
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引用次数: 0

摘要

依赖分析可以更好地帮助神经网络捕捉句子中的语义特征,从而提取实体关系。目前,人们提出了基于依赖树结构编码的硬剪枝策略和软剪枝策略,以平衡提取任务中有益的附加信息和不利的干扰。本文提出了一种基于图卷积网络的新模型,该模型使用多种表征从不同角度描述依存树,并将这些表征结合起来以获得更好的句子表征,用于关系分类。此外,还增加了一个新定义的模块,该模块利用注意力机制从上下文表征中捕捉更深层次的语义特征,作为输入文本的全局语义特征,从而帮助模型在句子层面捕捉更深层次的语义信息,用于关系提取任务。为了从输入句子中获取有关给定实体对的更多信息,作者还建立了实体的隐式共参(引用)模型。该模型可以最大限度地从句子中提取与实体间关系相关的语义特征。结果表明,本文中的模型在 SemEval2010-Task8 和 KBP37 数据集上取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Multiple dependence representation of attention graph convolutional network relation extraction model

Dependency analysis can better help neural network to capture semantic features in sentences, so as to extract entity relation. Currently, hard pruning strategies and soft pruning strategies based on dependency tree structure coding have been proposed to balance beneficial additional information and adverse interference in extraction tasks. A new model based on graph convolutional networks, which uses a variety of representations describing dependency trees from different perspectives and combining these representations to obtain a better sentence representation for relation classification is proposed. A newly defined module is added, and this module uses the attention mechanism to capture deeper semantic features from the context representation as the global semantic features of the input text, thus helping the model to capture deeper semantic information at the sentence level for relational extraction tasks. In order to get more information about a given entity pair from the input sentence, the authors also model implicit co-references (references) to entities. This model can extract semantic features related to the relationship between entities from sentences to the maximum extent. The results show that the model in this paper achieves good results on SemEval2010-Task8 and KBP37 datasets.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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