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graph convolutional networks Embedding Textual Structure Information for Relation Extraction

Computers, Materials & Continua 2024年第5期79卷 3299-3314页

作者： Chuyuan Wei Jinzhe Li Zhiyuan Wang Shanshan Wan Maozu Guo School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing102616China

Deep neural network-based relational extraction research has made significant progress in recent years,andit provides data support for many natural language processing downstream tasks such as building knowledgegraph,sentiment analysis and question-answering systems.However,previous studies ignored much unusedstructural information in sentences that could enhance the performance of the relation extraction task.Moreover,most existing dependency-based models utilize self-attention to distinguish the importance of context,whichhardly deals withmultiple-structure information.To efficiently leverage multiple structure information,this paperproposes a dynamic structure attention mechanism model based on textual structure information,which deeplyintegrates word embedding,named entity recognition labels,part of speech,dependency tree and dependency typeinto a graph convolutional network.Specifically,our model extracts text features of different structures from theinput sentence.Textual Structure information graph convolutional networks employs the dynamic structureattention mechanism to learn multi-structure attention,effectively distinguishing important contextual features invarious structural information.In addition,multi-structure weights are carefully designed as amergingmechanismin the different structure attention to dynamically adjust the final attention.This paper combines these featuresand trains a graph convolutional network for relation extraction.We experiment on supervised relation extractiondatasets including SemEval 2010 Task 8,TACRED,TACREV,and Re-TACED,the result significantly outperformsthe previous.

关键词： Relation extraction Graph convolutional neural networks dependency tree dynamic structure attention

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Geometric deep learning: progress, applications and challenges

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Science China(Information Sciences) 2022年第2期65卷 238-240页

作者： Wenming CAO Canta ZHENG Zhiyue YAN Weixin XIE College of Electronics and Information Engineering Shenzhen University

Deep learning technology,like convolutional neural networks,has been widely used in machine learning applications such as image detection and classification,speech processing,natural language processing and cross-modal retrieval[1].Although deep learning technology has achieved great success in various fields,it has been unable to match non-Euclidean data effectively.In this context,

关键词： Geometric deep learning manifold deep learning CNN Graph convolutional neural networks high-dimensional information

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Aspect-Level Sentiment Analysis Incorporating Semantic and Syntactic Information

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Journal of Computer and Communications 2024年第1期12卷 191-207页

作者： Jiachen Yang Yegang Li Hao Zhang Junpeng Hu Rujiang Bai School of Computer Science and Technology Shandong University of Technology Zibo China Institute of Information Management Shandong University of Technology Zibo China

Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.

关键词： Aspect-Level Sentiment Analysis Attentional Mechanisms Dependent Syntactic Trees Graph convolutional neural networks

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