Graph-based Deep Learning in Natural Language Processing

Shikhar Vashishth, Naganand Yadati, Partha Talukdar

Abstract
This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.
Anthology ID:
D19-2006
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/D19-2006
DOI:
Bibkey:
Cite (ACL):
Shikhar Vashishth, Naganand Yadati, and Partha Talukdar. 2019. Graph-based Deep Learning in Natural Language Processing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): Tutorial Abstracts, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Graph-based Deep Learning in Natural Language Processing (Vashishth et al., EMNLP-IJCNLP 2019)
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