A Biomedical Relation Extraction Method Based on Graph Convolutional Network with Dependency Information Fusion

IF 2.5 4区 综合性期刊 Q2 CHEMISTRY, MULTIDISCIPLINARY Applied Sciences-Basel Pub Date : 2023-09-06 DOI:10.3390/app131810055
Wanli Yang, L. Xing, Longbo Zhang, Hongzhen Cai, Maozu Guo
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Abstract

Biomedical texts are relatively obscure in describing relations between specialized entities, and the automatic extraction of drug–drug or drug–disease relations from massive biomedical texts presents a challenge faced by many researchers. To this end, this paper designs a relation extraction method based on dependency information fusion to improve the predictive power of the model for the relations between given biomedical entities. Firstly, we propose a local–global pruning strategy for the dependency syntax tree. Next, we propose the construction of a dependency type matrix for the pruned dependency tree to incorporate sentence dependency information into the model to feature extraction. We then incorporate attention mechanism into the graph convolutional model by calculating the attention weights of word–word dependencies, thus improving the traditional graph convolutional network. The model distinguishes the importance of different dependency information by attention weights, thus weakening the influence of interfering information such as word-to-word dependencies that are unrelated to entities in long sentences. In this paper, our proposed Dependency Information Fusion Attention Graph Convolutional Network (DIF-A-GCN) is evaluated on two biomedical datasets, DDI and CIVIC. The experimental results show that our proposed method based on dependency information fusion outperforms current state-of-the-art biomedical relation extraction models.
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基于依赖信息融合的图卷积网络的生物医学关系提取方法
生物医学文本在描述专业实体之间的关系方面相对模糊,从大量生物医学文本中自动提取药物-药物或药物-疾病关系是许多研究人员面临的挑战。为此,本文设计了一种基于依赖信息融合的关系提取方法,以提高模型对给定生物医学实体之间关系的预测能力。首先,我们提出了依赖句法树的局部-全局剪枝策略。接下来,我们提出构建一个依赖类型矩阵,将句子依赖信息整合到模型中进行特征提取。然后通过计算词-词依赖关系的注意权值,将注意机制融入到图卷积模型中,从而改进了传统的图卷积网络。该模型通过注意权重来区分不同依赖信息的重要性,从而削弱了长句中与实体无关的词对词依赖等干扰信息的影响。本文在DDI和CIVIC两个生物医学数据集上对我们提出的依赖信息融合注意图卷积网络(DIF-A-GCN)进行了评估。实验结果表明,基于依赖信息融合的生物医学关系提取方法优于现有的生物医学关系提取模型。
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来源期刊
Applied Sciences-Basel
Applied Sciences-Basel CHEMISTRY, MULTIDISCIPLINARYMATERIALS SCIE-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
5.30
自引率
11.10%
发文量
10882
期刊介绍: Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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