Hetegcn: heterogeneous graph convolutional networks for text classification

R Ragesh, S Sellamanickam, A Iyer, R Bairi… - Proceedings of the 14th …, 2021 - dl.acm.org
We consider the problem of learning efficient and inductive graph convolutional networks for
text classification with a large number of examples and features. Existing state-of-the-art …
被引用次数：88 相关文章

HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification

R Ragesh, S Sellamanickam, A Iyer, R Bairi… - arXiv preprint arXiv …, 2020 - arxiv.org
We consider the problem of learning efficient and inductive graph convolutional networks for
text classification with a large number of examples and features. Existing state-of-the-art …
[PDF] researchgate.net

[PDF][PDF] HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification

R Ragesh, S Sellamanickam, A Iyer, R Bairi, V Lingam - 2021 - researchgate.net
We consider the problem of learning efficient and inductive graph convolutional networks for
text classification with a large number of examples and features. Existing state-of-the-art …

HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification

R Ragesh, S Sellamanickam, A Iyer, R Bairi… - arXiv e …, 2020 - ui.adsabs.harvard.edu
We consider the problem of learning efficient and inductive graph convolutional networks for
text classification with a large number of examples and features. Existing state-of-the-art …
[PDF] researchgate.net

[PDF][PDF] HeteGCN: Heterogeneous Graph Convolutional Networks for Text Classification

R Ragesh, S Sellamanickam, A Iyer, R Bairi, V Lingam - researchgate.net
We consider the problem of learning efficient and inductive graph convolutional networks for
text classification with a large number of examples and features. Existing state-of-the-art …