Deep Learning on Graphs for Natural Language Processing

Lingfei Wu, Yu Chen, Heng Ji, Bang Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


There are a rich variety of NLP problems that can be best expressed with graph structures. Due to the great power in modeling non-Euclidean data like graphs, deep learning on graphs techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems, and have already achieved great success. Despite the success, deep learning on graphs for NLP (DLG4NLP) still faces many challenges (e.g., automatic graph construction, graph representation learning for complex graphs, learning mapping between complex data structures). This tutorial will cover relevant and interesting topics on applying deep learning on graph techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic parsing). In addition, hands-on demonstration sessions will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library - Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.

Original languageEnglish (US)
Title of host publicationKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Number of pages2
ISBN (Electronic)9781450383325
StatePublished - Aug 14 2021
Event27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Duration: Aug 14 2021Aug 18 2021

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining


Conference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
CityVirtual, Online


  • deep learning
  • graph learning
  • graph neural networks
  • natural language processing

ASJC Scopus subject areas

  • Software
  • Information Systems


Dive into the research topics of 'Deep Learning on Graphs for Natural Language Processing'. Together they form a unique fingerprint.

Cite this