SK-GCN: Modeling Syntax and Knowledge via Graph Convolutional Network for aspect-level sentiment classification

Zhou, Jie; Huang, Jimmy Xiangji; Hu, Qinmin; He, Liang

doi:10.1016/j.knosys.2020.106292

Cited by 169 publications

(68 citation statements)

References 38 publications

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“…Results: To demonstrate the effectiveness of the proposed method, we compare it with the following baselines: (1) the feature-based model that applies feature engineering and the SVM model (Wagner et al, 2014), (2) the deep learning models based on the sequential order of the words in the sentences, including CNN, LSTM, attention and the gating mechanism (Wagner et al, 2016;Wang et al, 2016;Tang et al, 2016;Huang et al, 2018;, and (3) the graph-based models that exploit dependency trees to improve the deep learning models for ABSA (Huang and Carley, 2019;Hou et al, 2019;Wang et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The current state-of-the-art deep learning models for ABSA feature the graph-based models where the dependency trees are leveraged to improve the performance. (Huang and Carley, 2019;Hou et al, 2019). However, to the best of our knowledge, none of these works has used the information from the aspect term to filter the graph-based hidden vectors and exploited importance scores for words from dependency trees as we do in this work.…”

Section: Related Workmentioning

confidence: 99%

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

Veyseh

Nouri²,

Dernoncourt

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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Aspect-based Sentiment Analysis (ABSA) seeks to predict the sentiment polarity of a sentence toward a specific aspect. Recently, it has been shown that dependency trees can be integrated into deep learning models to produce the state-of-the-art performance for ABSA. However, these models tend to compute the hidden/representation vectors without considering the aspect terms and fail to benefit from the overall contextual importance scores of the words that can be obtained from the dependency tree for ABSA. In this work, we propose a novel graph-based deep learning model to overcome these two issues of the prior work on ABSA. In our model, gate vectors are generated from the representation vectors of the aspect terms to customize the hidden vectors of the graph-based models toward the aspect terms. In addition, we propose 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. The proposed model achieves the state-of-the-art performance on three benchmark datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Veyseh

Nouri²,

Dernoncourt

et al. 2020

Findings of the Association for Computational Linguistics: EMNLP 2020

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show abstract

“…proposed a GCN model over the dependency tree of the sentence to enhance the feature representations of aspects learned by a Bi-directional LSTM (Bi-LSTM). In addition, to develop the merit of BERT (Devlin et al, 2019), a GCN model based on selective attention was proposed to extract and aggregate the most important contextual features for the aspect representation (Hou et al, 2019). The above GCN-based models, however, neither considered the specific aspect when constructing the graph of the sentence nor extracted inter-aspect sentiment relations for the specific aspect.…”

Section: Related Workmentioning

confidence: 99%

“…AEN+BERT is the AEN model based on pre-trained BERT. SA-GCN+BERT (Hou et al, 2019) is a GCN-based model with dependency tree and BERT, which employs the selective attention find important words to derive the representations of aspects. InterGCN is our complete proposed model.…”

Section: Comparison Modelsmentioning

confidence: 99%

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis

Liang

Yin

Gui

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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In this paper, we explore a novel solution of constructing a heterogeneous graph for each instance by leveraging aspect-focused and inter-aspect contextual dependencies for the specific aspect. Based on it, we propose a novel graph-aware model with Interactive Graph Convolutional Networks (InterGCN) for aspect sentiment analysis. Specifically, an ordinary dependency graph is first constructed for each sentence over the dependency tree. Then we refine the graph by considering the syntactical dependencies between contextual words and aspect-specific words to derive the aspect-focused graph. Subsequently, the aspect-focused graph and the corresponding embedding matrix are fed into the aspect-focused GCN to capture the key aspect and contextual words. Besides, to interactively extract the dependencies between the aspect words and other aspects, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph. Hence, the model can be aware of the significant contextual and aspect words when interactively learning the sentiment features for a specific aspect. Experimental results on four benchmark datasets illustrate that our proposed model outperforms state-of-the-art methods and substantially boosts the performance in comparison with BERT.

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“…of NLP tasks. For example, some researchers incorporated a hard constraint into SANs to select a subset of input words, on top of which self-attention is conducted (Shen et al, 2018c;Hou et al, 2019;Yang et al, 2019b). and Guo et al (2019) proposed a soft mechanism by imposing a learned Gaussian bias over the original attention distribution to enhance its ability of capturing local contexts.…”

Section: Introductionmentioning

confidence: 99%

How Does Selective Mechanism Improve Self-Attention Networks?

Geng¹,

Wang

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words. However, the underlying reasons for their strong performance have not been well explained. In this paper, we bridge the gap by assessing the strengths of selective SANs (SSANs), which are implemented with a flexible and universal Gumbel-Softmax. Experimental results on several representative NLP tasks, including natural language inference, semantic role labelling, and machine translation, show that SSANs consistently outperform the standard SANs. Through well-designed probing experiments, we empirically validate that the improvement of SSANs can be attributed in part to mitigating two commonly-cited weaknesses of SANs: word order encoding and structure modeling. Specifically, the selective mechanism improves SANs by paying more attention to content words that contribute to the meaning of the sentence. The code and data are released at https://github.com/xwgeng/SSAN.

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SK-GCN: Modeling Syntax and Knowledge via Graph Convolutional Network for aspect-level sentiment classification

Cited by 169 publications

References 38 publications

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

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

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis

How Does Selective Mechanism Improve Self-Attention Networks?

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