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A Multiple Graphical Convolution Networks Approach for Aspect-Based Sentiment Classification

delete2026-01-01
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PRE
AI
D
Diana Kazakova
G
Gdovinova, Zuzana *
J
Jasmijn Klinkhamer
DOI：10.1007/978-3-031-97141-9_5delete
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摘要

摘要

En 中文
基于方面的情感分类（ABSC）近年来吸引了大量关注。一些ABSC的最佳表现模型是混合模型和基于深度学习的模型。本研究提出了一种ABSC模型HAABSA4GCN，作为当前最先进的混合模型HAABSA++的扩展。HAABSA4GCN模型结合了HAABSA++中的领域本体与一种基于图卷积神经网络（GCN）的新型备份模型4GCN。该备份模型使用四种图：句法图、语义图、词汇图和本体图。在评估中使用了SemEval 2015和SemEval 2016数据集。评估结果表明，HAABSA4GCN优于HAABSA++，表明模型中不同图的组合提高了预测精度。此外，4GCN的表现优于HAABSA4GCN，暗示4GCN在处理领域本体可以决定的情况时更为出色。
Keyword:
Aspect-based sentiment classification
Graph convolution neural network
Domain ontology

期刊

N
NATURAL LANGUAGE PROCESSING AND INFORMATION SYSTEMS, NLDB 2025, PT I
IF:
0
论文数:
30
被引数:
0

机构

E
erasmus university rotterdam
学者数:
2.8K
论文数: 1.2K
被引数: 0
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