ML Anthology

Graph Neural Networks for Natural Language Processing: A Survey

Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long
FTML 2023 pp. 119-328
doi:10.1561/2200000096 /ftml/2023/wu2023ftml-graph/

Abstract

Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be best expressed with a graph structure. As a result, there is a surge of interest in developing new deep learning techniques on graphs for a large number of NLP tasks. In this survey, we present a comprehensive overview on Graph Neural Networks (GNNs) for Natural Language Processing. We propose a new taxonomy of GNNs for NLP, which systematically organizes existing research of GNNs for NLP

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Cite

Text

Wu et al. "Graph Neural Networks for Natural Language Processing: A Survey." Foundations and Trends in Machine Learning, 2023. doi:10.1561/2200000096

Markdown

[Wu et al. "Graph Neural Networks for Natural Language Processing: A Survey." Foundations and Trends in Machine Learning, 2023.](https://mlanthology.org/ftml/2023/wu2023ftml-graph/) doi:10.1561/2200000096

BibTeX

@article{wu2023ftml-graph,
  title     = {{Graph Neural Networks for Natural Language Processing: A Survey}},
  author    = {Wu, Lingfei and Chen, Yu and Shen, Kai and Guo, Xiaojie and Gao, Hanning and Li, Shucheng and Pei, Jian and Long, Bo},
  journal   = {Foundations and Trends in Machine Learning},
  year      = {2023},
  pages     = {119-328},
  doi       = {10.1561/2200000096},
  volume    = {16},
  url       = {https://mlanthology.org/ftml/2023/wu2023ftml-graph/}
}