Deep Learning on Graphs for Natural Language Processing

Lingfei Wu, Yu Chen, Heng Ji

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

Abstract

This tutorial of Deep Learning on Graphs for Natural Language Processing (DLG4NLP) 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, a handson demonstration session 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 publicationSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages2651-2653
Number of pages3
ISBN (Electronic)9781450380379
DOIs
StatePublished - Jul 11 2021
Event44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021 - Virtual, Online, Canada
Duration: Jul 11 2021Jul 15 2021

Publication series

NameSIGIR 2021 - Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2021
Country/TerritoryCanada
CityVirtual, Online
Period7/11/217/15/21

Keywords

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

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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