Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks

Huang, Binxuan; Carley, Kathleen M.

doi:10.18653/v1/d19-1549

Cited by 232 publications

(152 citation statements)

References 31 publications

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“…For example, Xue ane Li [40] proposed a CNN with the gate mechanisms to learn the representations for aspects and the opinion words. Huang and Carley [41] proposed to take the aspect information into account and adopt CNN for ALSC. Xing et al [42] incorporated attention-based input layers into CNN to introduce context words information.…”

Section: A Aspect-level Sentiment Classificationmentioning

confidence: 99%

Syntactic Edge-Enhanced Graph Convolutional Networks for Aspect-Level Sentiment Classification With Interactive Attention

Xiao

Zhou

2020

IEEE Access

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Aspect-level sentiment classification is a hot research topic in natural language processing (NLP). One of the key challenges is that how to develop effective algorithms to model the relationships between aspects and opinion words appeared in a sentence. Among the various methods proposed in the literature, the graph convolutional networks (GCNs) achieve the promising results due to their good ability to capture the long distance between the aspects and the opinion words. However, the existing methods cannot effectively leverage the edge information of dependency parsing tree, resulting in the sub-optimal results. In this article, we propose a syntactic edge-enhanced graph convolutional network (ASEGCN) for aspect-level sentiment classification with interactive attention. Our proposed method can effectively learn better representations of aspects and the opinion words by considering the different types of neighborhoods with the edge constraint. To evaluate the effectiveness of our proposed method, we conduct the experiments on five standard sentiment classification results. Our results demonstrate that our proposed method obtains the better performance than the state-of-the-art models on four datasets, and achieves a comparative performance on Rest16.

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Section: A Aspect-level Sentiment Classificationmentioning

confidence: 99%

Syntactic Edge-Enhanced Graph Convolutional Networks for Aspect-Level Sentiment Classification With Interactive Attention

Xiao

Zhou

2020

IEEE Access

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“…They also apply pre-trained BERT and obtain new state-of-the-art results on SemEval 2014 dataset. Huang and Carley [32] propose a novel target-dependent graph attention network (TD-GAT) for ABSA by explicitly utilizing the dependency relationship among words and also demonstrate that using BERT representations further substantially boosts the performance.…”

Section: Related Work a Pre-training Language Modelsmentioning

confidence: 99%

“…From the above results, we found that the performance of these two XLNetCN models is unsteady on the conditions of different iteration numbers and capsule number settings. But generally, they both reach the best when iteration is set to 3 and the pair (cn, d c ) is (24,32). So, in the following experiments we will take 3 as the default value for the iteration number n, and (24, 32) for (cn, d c ) without further explicit mention.…”

Section: A Experiments 1: Effects Of Iterative Routing Analysismentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Jiankang Zhang . features (e.g., [5]- [7]) or attention mechanisms (e.g., [3], [4], [8]- [11]). More recently, some scholars even propose complicated models with the help of more advanced neural structures, e.g., transformation networks [12], parameterized convolutional neural networks (PCNN) [13], gated convolutional networks (GCN) [14], memory networks [15], graph networks [16], [17] or semantic cognition networks (SCN) [18]. However, just as Li et al pointed out in [19], the improvement of these models measured by the accuracy or F1 score has reached a bottleneck, because the commonly used embeddings are pre-trained via word2vec or GloVe, which can only provide context-independent word-level features and are insufficient for capturing the complex semantic dependencies in the sentence.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Aspect-Based Sentiment Analysis With Capsule Network

Luo

2020

IEEE Access

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Existing feature-based neural approaches for aspect-based sentiment analysis (ABSA) try to improve their performance with pre-trained word embeddings and by modeling the relations between the text sequence and the aspect (or category), thus heavily depending on the quality of word embeddings and task-specific architectures. Although the recently pre-trained language models, i.e., BERT and XLNet, have achieved state-of-the-art performance in a variety of natural language processing (NLP) tasks, they still subject to the aspect-specific, local feature-aware and task-agnostic challenges. To address these challenges, this paper proposes a XLNet and capsule network based model XLNetCN for ABSA. XLNetCN firstly constructs auxiliary sentence to model the sequence-aspect relation and generate global aspect-specific representations, which enables to enhance aspect-awareness and ensure the full pre-training of XLNet for improving task-agnostic capability. After that, XLNetCN also employs a capsule network with the dynamic routing algorithm to extract the local and spatial hierarchical relations of the text sequence, and yield its local feature representations, which are then merged with the global aspect-related representations for downstream classification via a softmax classifier. Experimental results show that XLNetCN outperforms significantly than the classical BERT, XLNet and traditional feature-based approaches on the two benchmark datasets of SemEval 2014, Laptop and Restaurant, and achieves new state-of-the-art results. INDEX TERMS Aspect-based sentiment analysis, natural language processing, text analysis, deep learning, neural network.

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“…On the other hand, in the syntactic dependency tree, the word "delicious" is closer to the target "hotpot" (see Figure 1). In addition, the use of syntactic dependency trees also helps to solve the potential ambiguity in word sequences [16]. In the simple sentence "nice beef terrible juice", nice and terrible can be used interchangeably.…”

Section: Introductionmentioning

confidence: 99%

Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks

Xiao

Chen

et al. 2020

Applied Sciences

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Targeted sentiment classification aims to predict the emotional trend of a specific goal. Currently, most methods (e.g., recurrent neural networks and convolutional neural networks combined with an attention mechanism) are not able to fully capture the semantic information of the context and they also lack a mechanism to explain the relevant syntactical constraints and long-range word dependencies. Therefore, syntactically irrelevant context words may mistakenly be recognized as clues to predict the target sentiment. To tackle these problems, this paper considers that the semantic information, syntactic information, and their interaction information are very crucial to targeted sentiment analysis, and propose an attentional-encoding-based graph convolutional network (AEGCN) model. Our proposed model is mainly composed of multi-head attention and an improved graph convolutional network built over the dependency tree of a sentence. Pre-trained BERT is applied to this task, and new state-of-art performance is achieved. Experiments on five datasets show the effectiveness of the model proposed in this paper compared with a series of the latest models.

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Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks

Cited by 232 publications

References 31 publications

Syntactic Edge-Enhanced Graph Convolutional Networks for Aspect-Level Sentiment Classification With Interactive Attention

Syntactic Edge-Enhanced Graph Convolutional Networks for Aspect-Level Sentiment Classification With Interactive Attention

Enhancing Aspect-Based Sentiment Analysis With Capsule Network

Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks

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