Domain|Adversarial Graph Neural Networks for Text Classification

Domain-Adversarial Graph Neural Networks for Text Classification

This paper proposes an end-to-end, domain-adversarial graph neural networks (DAGNN), for cross- domain text classification. Our motivation is to model documents.

Domain-Adversarial Graph Neural Networks for Text Classification

This paper proposes an end-to-end, domain-adversarial graph neural networks (DAGNN), for cross-domain text classification.

Domain-Adversarial Graph Neural Networks for Text Classification

... adversarial graph neural networks (DAGNN), for cross-domain text classification. Our motivation is to model documents as graphs and use a domain-adversarial ...

Domain-Adversarial Graph Neural Networks for Text Classification

At the feature level, DAGNN uses graphs from different domains to jointly train hierarchical graph neural networks in order to learn good features. At the ...

Graph neural networks for text classification: a survey

Topic classification is a supervised deep learning task to automatically understand the text content and classify it into multiple domain- ...

Domain-Adversarial Graph Neural Networks for Text Classification

2019 IEEE International Conference on Data Mining (ICDM). 648. Page 2. 649. Page 3. 650. Page 4. 651. Page 5. 652. Page 6. 653. Page 7. 654. Page 8 ...

Graph Neural Networks for Text Classification: A Survey - arXiv

Topic Classification is a supervised deep learning task to automatically understand the text content and classified into multiple domain-specific categories, ...

Domain adversarial graph neural network with cross-city graph ...

All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the ...

Revisiting Adversarial Attacks on Graph Neural Networks for ... - arXiv

Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world ...

Domain-adversarial graph neural networks for Λ hyperon ...

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Text Classification on Imbalanced Data using Graph Neural ...

Importantly, we address the challenge of class imbalance using an adversarial loss framework. We introduce separate weight generator networks for each class ...

Text Level Graph Neural Network for Text Classification

So GNN are proposed (Scarselli et al.,. 2009) to apply deep learning techniques to data in graph domain. 4.2 Text Classification. Text classification is a ...

kaize0409/Awesome-Graph-OOD - GitHub

Invariant Representation Learning, [ICDM 2019] Domain-Adversarial Graph Neural Networks for Text Classification, [N/A]. DANE, Invariant Representation Learning ...

Contrastive knowledge integrated graph neural networks for ...

... domain knowledge. ... In this paper, we propose contrastive knowledge integrated graph neural networks (ConKGNN) for Chinese medical text classification.

Dual Adversarial Graph Neural Networks for Multi-label Cross ...

The learned label classifiers are then employed to classify the common representation, which is generated by the Dual. GAN, to perform the end-to-end training ...

Text Classification Model Based on Graph Attention Networks and ...

Text Classification Model Based on Graph Attention Networks and Adversarial Training ... Deep Attention Diffusion Graph Neural Networks for Text Classification.

(PDF) Graph Neural Networks for Text Classification: A Survey

While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly ...

How to Use Graph Neural Networks for Text Classification?

Table of Contents · Deep learning in Text Classification · Graph Neural Networks · Graph · Method of Graph Neural Network · Graph Convolutional ...

Graph Neural Network and Some of GNN Applications - neptune.ai

It's like image classification, but the target changes into the graph domain. ... A classic application of GNNs in NLP is Text Classification.

Graph Neural Networks for Text Classification: A Survey - OUCI

While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with ...