Knowledge-guided multi-granularity GCN for ABSA

Zhu, Zhenfang; Zhang, Dianyuan; Li, Lin; Li, Kefeng; Qi, Jiangtao; Wang, Wenling; Zhang, Guangyuan; Liu, Peiyu

doi:10.1016/j.ipm.2022.103223

Cited by 34 publications

(7 citation statements)

References 34 publications

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“…Gandhi et al [6] used conditional random fields and bi-directional LSTM to extract aspectual terms from text and model their sentiment. Utilizing an attention-based approach, a multi-granular attention mechanism was implemented by Zhu et al [24] with the aim of enhancing the dependence among aspects and words of opinion. Additionally, Xue and Li [22] utilized a gating mechanism in a gated CNN model to output sentiment information exclusively, in accordance with a specified aspect.…”

Section: Aspect-based Sentiment Analysismentioning

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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Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

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

Song,

Ling,

et al. 2024

Electronics

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“…As a sentiment-analysis-related task, the result based on only the identification of textimage incongruity may be unreliable. Notably, current sentiment analysis studies reveal that the exploitation of semantics provides a deep-level understanding, which substantially benefits the sentiment polarity prediction [9,10,21]. For this reason, an Intra-modal GNN is designed to capture the semantic information within the sentence.…”

Section: Semantic-enhanced Modulementioning

confidence: 99%

A Semantic Enhancement Framework for Multimodal Sarcasm Detection

Zhong,

Zhang,

et al. 2024

Mathematics

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Sarcasm represents a language form where a discrepancy lies between the literal meanings and implied intention. Sarcasm detection is challenging with unimodal text without clearly understanding the context, based on which multimodal information is introduced to benefit detection. However, current approaches only focus on modeling text–image incongruity at the token level and use the incongruity as the key to detection, ignoring the significance of the overall multimodal features and textual semantics during processing. Moreover, semantic information from other samples with a similar manner of expression also facilitates sarcasm detection. In this work, a semantic enhancement framework is proposed to address image–text congruity by modeling textual and visual information at the multi-scale and multi-span token level. The efficacy of textual semantics in multimodal sarcasm detection is pronounced. Aiming to bridge the cross-modal semantic gap, semantic enhancement is performed by using a multiple contrastive learning strategy. Experiments were conducted on a benchmark dataset. Our model outperforms the latest baseline by 1.87% in terms of the F1-score and 1% in terms of accuracy.

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“…Transfer learning involves transferring the knowledge or patterns learned from existing labeled training data to improve learning in a new target field (Weiss et al, 2016). Incorporating transfer learning in the deep learning model training process not only accelerates the model training process but also facilitates the acquisition of a more accurate deep learning model through the fine-tuning of the pre-trained model (Zhu et al, 2023). In current research on plant disease and pest identification, many researchers have applied transfer learning to CNN models to improve both the training speed of the model and the accuracy of identification (Thenmozhi and Reddy, 2019;Liu et al, 2022).…”

Section: Transfer Learningmentioning

confidence: 99%

A lightweight model for efficient identification of plant diseases and pests based on deep learning

Guan

Zhang

et al. 2023

Front. Plant Sci.

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Plant diseases and pests have always been major contributors to losses that occur in agriculture. Currently, the use of deep learning-based convolutional neural network models allows for the accurate identification of different types of plant diseases and pests. To enable more efficient identification of plant diseases and pests, we design a novel network architecture called Dise-Efficient based on the EfficientNetV2 model. Our experiments demonstrate that training this model using a dynamic learning rate decay strategy can improve the accuracy of plant disease and pest identification. Furthermore, to improve the model’s generalization ability, transfer learning is incorporated into the training process. Experimental results indicate that the Dise-Efficient model boasts a compact size of 13.3 MB. After being trained using the dynamic learning rate decay strategy, the model achieves an accuracy of 99.80% on the Plant Village plant disease and pest dataset. Moreover, through transfer learning on the IP102 dataset, which represents real-world environmental conditions, the Dise-Efficient model achieves a recognition accuracy of 64.40% for plant disease and pest identification. In light of these results, the proposed Dise-Efficient model holds great potential as a valuable reference for the deployment of automatic plant disease and pest identification applications on mobile and embedded devices in the future.

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Knowledge-guided multi-granularity GCN for ABSA

Cited by 34 publications

References 34 publications

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

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

A Semantic Enhancement Framework for Multimodal Sarcasm Detection

A lightweight model for efficient identification of plant diseases and pests based on deep learning

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