Hierarchical graph augmented stacked autoencoders for multi-view representation learning

Gou, Jianping; Xie, Nannan; Liu, Jinhua; Yu, Baosheng; Ou, Weihua; Yi, Zhang; Chen, Wu

doi:10.1016/j.inffus.2023.102068

Cited by 4 publications

(1 citation statement)

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“…Sun et al used a BERT-based model to capture semantic features from contexts via fine-tuning, which significantly improves the working performance [16]. Text encoders are widely applied to various tasks [17,18]. Encouragingly, advances in contrastive learning hold great potential in natural language processing (NLP) tasks.…”

Section: Contextual Information Learningmentioning

confidence: 99%

A dual contrastive learning-based graph convolutional network with syntax label enhancement for aspect-based sentiment classification

Huang,

Dai,

Cao

et al. 2024

Front. Phys.

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Introduction: Aspect-based sentiment classification is a fine-grained sentiment classification task. State-of-the-art approaches in this field leverage graph neural networks to integrate sentence syntax dependency. However, current methods fail to exploit the data augmentation in encoding and ignore the syntactic relation in sentiment delivery.Methods: In this work, we propose a novel graph neural network-based architecture with dual contrastive learning and syntax label enhancement. Specifically, a contrastive learning-based contextual encoder is designed, integrating sentiment information for semantics learning. Moreover, a weighted label-enhanced syntactic graph neural network is established to use both the syntactic relation and syntax dependency, which optimizes the syntactic weight between words. A syntactic triplet between words is generated. A syntax label-based contrastive learning scheme is developed to map the triplets into a unified feature space for syntactic information learning.Results: Experiments on five publicly available datasets show that our model substantially outperforms the baseline methods.Discussion: As such, the proposed method shows its effectiveness in aspect-based sentiment classification tasks.

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Section: Contextual Information Learningmentioning

confidence: 99%