知识增强的双通道多头GCN用于方面级情感分析

doi:10.3778/j.issn.1002-8331.2306-0126

摘要/Abstract

摘要： 方面级情感分析（aspect based sentiment analysis，ABSA）是自然语言处理领域的一个重要任务，其目标是对句子中给定的方面词进行情感极性的判断。目前，最先进的ABSA模型采用图神经网络处理句子的语义信息和句法结构。然而，这些方法对句法依赖树蕴含的信息使用不足，不仅缺少对外部知识的挖掘，而且忽略了对模型引入上下文噪声的消除。针对这些问题，提出了一种知识增强的双通道多头图卷积神经网络。该模型建立了基于语义的多头图卷积网络和基于句法的多头图卷积网络，利用外部情感知识以及句法依赖距离重构句法依赖树，使模型充分融入外部知识。同时采用自注意力机制构建动态语义图并过滤引入噪声，从而更多地关注方面词。模型在3个公开基准数据集Rest14、Lap14、Twitter上的准确率分别达到了87.57%、82.34%、77.75%，显著优于基线模型。

关键词: 方面级情感分析, 外部知识, 多头图卷积, 自注意力, 句法依赖距离

Abstract: Aspect-based sentiment analysis (ABSA) is an important task in the field of natural language processing, and its goal is to classify the sentiment polarity of a given aspect word in a sentence. The current state-of-the-art ABSA model uses a graph neural network to process the semantic information and syntactic structure of sentences. However, these methods make insufficient use of syntactic dependency tree implication information, lack of mining of external knowledge, and ignore the removal of contextual noise introduced by the model. To address these issues, a knowledge enhanced dual-channel multi-head graph convolutional neural network is proposed. This model builds a semantic-based multi-head graph convolutional network and a syntax-based multi-head graph convolutional network. Using external emotional knowledge and syntactic dependency distance to reconstruct the syntactic dependency tree, so that the model can fully integrate external knowledge. A self-attention mechanism is used to construct a dynamic semantic map and filter the introduced noise, so as to pay more attention to aspect words. The accuracy of the model on the three public benchmark datasets Rest14, Lap14, and Twitter reaches 87.57%, 82.34%, and 77.75%, respectively, which is significantly better than the baseline model.

Key words: aspect-based sentiment analysis, external knowledge, multi-head graph convolution, self-attention mechanism, syntax-dependent distance

谢泽, 陈庆锋, 莫少聪, 刘春雨, 邱俊铼. 知识增强的双通道多头GCN用于方面级情感分析[J]. 计算机工程与应用, 2024, 60(17): 98-106.

XIE Ze, CHEN Qingfeng, MO Shaocong, LIU chunyu, QIU Junlai. Knowledge Enhanced Dual-Channel Multi-Head Graph Convolutional Networks for Aspect-Based Sentiment Analysis[J]. Computer Engineering and Applications, 2024, 60(17): 98-106.

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