Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation

Veyseh, Amir Pouran Ben; Nasim, Nour,; Dernoncourt, Franck; Tran, Quan Hung; Dou, Dejing; Nguyen, Thien Huu

doi:10.48550/arxiv.2010.13389

Cited by 3 publications

(3 citation statements)

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“…Li et al [22] proposed DualGCN networks model which constructed a Syn-GCN and SemGGN utilizing syntactic and semantic information, respectively, in the aspect-based sentiment analysis task shown that DualGCN model outperforms baselines. Veyseh et al [23] rethought the noise of dependency information and proposed a novel GCN-based gated mechanism for the same task. Zhang et al [24] used joint learning for the tasks of semantic and opinion role labeling, using a GCN to share parameters.…”

Section: B Graph Convolutional Neural Networkmentioning

confidence: 99%

Multiple POS Dependency-Aware Mixture of Experts for Frame Identification

Yan

Chai

et al. 2023

IEEE Access

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Frame identification, which is finding the exact evoked frame for a target word in a given sentence, is a fundamental and crucial prerequisite for frame semantic parsing. It is generally seen as a classification task for target words, whose contextual representations are usually obtained using a neural network like BERT as an encoder, and enriched with a joint learning model or the knowledge of FrameNet. However, the distinction at a fine-grained level, such as the delicate differences in the information of syntax and PropBank roles caused by different parts-of-speech (POS) of target words, is neglected. We propose a Multiple POS Dependency-aware Mixture of Experts(MPDaMoE) network that integrates five types of information, consisting of the syntactic information of target words whose POS are nominal, adjectival, adverbial, or prepositional, and the PropBank role information of target words whose POS are only verbal.To better learn such information, a Mixture of Experts network is employed, in which every expert is a Graph Convolutional Network, to incorporate the different dependency information of target words. Our model outperforms state-of-the-art models in experiments on two benchmark datasets, which shows its effectiveness.

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Section: B Graph Convolutional Neural Networkmentioning

confidence: 99%

Multiple POS Dependency-Aware Mixture of Experts for Frame Identification

Yan

Chai

et al. 2023

IEEE Access

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“…• DM + GCN + BERT [55]: Performs dynamic and multi-channel GCN modeling of syntactic and semantic information in sentences. • SGGCN + BERT [56]: Alters the graph-based model's hidden vectors to make the most of information from the aspects. • AIEN + BERT [57]: Constructs an interaction encoder using a GCN and attention mechanisms for extracting interaction features.…”

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confidence: 99%

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

Song,

Ling,

et al. 2024

Electronics

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The purpose of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity of aspects in a given sentence. Most historical works on sentiment analysis used complex and inefficient methods to integrate external knowledge. Furthermore, they fell short of completely utilizing BERT’s potential because when trying to generate word embeddings, they merely averaged the BERT subword vectors. To overcome these limitations, we propose a knowledge-guided heterogeneous graph convolutional network for aspect-based sentiment analysis (KHGCN). Specifically, we consider merging subword vectors utilizing a dynamic weight mechanism in the BERT embedding layer. Additionally, heterogeneous graphs are constructed to fuse different feature associations between words, and graph convolutional networks are utilized to identify context-specific syntactic features. Furthermore, by embedding a knowledge graph, the model can learn additional features from sources other than the corpus. Based on this knowledge, it is consequently possible to obtain more knowledge representation for a particular aspect by utilizing the attention mechanism. Last but not least, semantic features, syntactic features, and knowledge are dynamically combined using feature fusion. Experiments on three public datasets demonstrate that our model achieves accuracy rates of 80.87%, 85.42%, and 91.07%, which is an improvement of more than 2% compared to other benchmark models based on HGCNs and BERT.

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“…• MIMLLN+BERT [33]: proposes a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category. • SGGCN+BERT [34]: proposes a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA. • DGEDT+BERT [35]: proposes a dependency graph enhanced dual-transformer network (named DGEDT) by jointly considering the flat representations learned from Transformer and graph-based representations learned from the corresponding dependency graph in an iterative interaction manner.…”

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confidence: 99%

Dual-Channel Edge-Featured Graph Attention Networks for Aspect-Based Sentiment Analysis

Shi

Liu

et al. 2023

Electronics

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The goal of aspect-based sentiment analysis (ABSA) is to identify the sentiment polarity of specific aspects in a context. Recently, graph neural networks have employed dependent tree syntactic information to assess the link between aspects and contextual words; nevertheless, most of this research has neglected phrases that are insensitive to syntactic analysis and the effect between various aspects in a sentence. In this paper, we propose a dual-channel edge-featured graph attention networks model (AS-EGAT), which builds an aspect syntactic graph by enhancing the contextual syntactic dependency representation of key aspect words and the mutual affective relationship between various aspects in the context and builds a semantic graph through the self-attention mechanism. We use the edge features as a significant factor to determine the weight coefficient of the attention mechanism to efficiently mine the edge features of the graph attention networks model (GAT). As a result, the model can connect important sentiment features of related aspects when dealing with aspects that lack obvious sentiment expressions, pay close attention to important word aspects when dealing with multiple-word aspects, and extract sentiment features from sentences that are not sensitive to syntactic dependency trees by looking at semantic features. Experimental results show that our proposed AS-EGAT model is superior to the current state-of-the-art baselines. Compared with the baseline models of LAP14, REST15, REST16, MAMS, T-shirt, and Television datasets, the accuracy of our AS-EGAT model increased by 0.76%, 0.29%, 0.05%, 0.15%, 0.22%, and 0.38%, respectively. The macro-f1 score increased by 1.16%, 1.16%, 1.23%, 0.37%, 0.53%, and 1.93% respectively.

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Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation

Cited by 3 publications

References 0 publications

Multiple POS Dependency-Aware Mixture of Experts for Frame Identification

Multiple POS Dependency-Aware Mixture of Experts for Frame Identification

Knowledge-Guided Heterogeneous Graph Convolutional Network for Aspect-Based Sentiment Analysis

Dual-Channel Edge-Featured Graph Attention Networks for Aspect-Based Sentiment Analysis

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