Paper Info
Title
Deep Learning on Graphs for Natural Language Processing
Abstract
ABSTRACTThere 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.
Year
DOI
Venue
2021
10.1145/3447548.3470820
Knowledge Discovery and Data Mining
Keywords
DocType
Citations 
Natural Language Processing, Deep Learning, Graph Learning, Graph Neural Networks
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Lingfei Wu111632.05
Yu Chen2144.27
Heng Ji31544127.27
Liu Bang422.41