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Tutorial: Graph Neural Networks for Natural Language Processing at EMNLP 2019 and CODS-COMAD 2020
| Date | Stars |
|---|---|
| 2026-07-24 | 790 |
| 2026-07-25 | 790 |
| 2026-07-28 | 790 |
| 2026-07-30 | 790 |
| 2026-07-31 | 790 |
| 2026-08-02 | 790 |
| 2026-08-06 | 790 |
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# Graph Neural Networks for Natural Language Processing
[](https://www.emnlp-ijcnlp2019.org/program/tutorials/)
[](https://cods-comad.in/)
[](https://shikhar-vashishth.github.io/assets/pdf/emnlp19_tutorial.pdf)
The repository contains code examples for [GNN-for-NLP](https://www.emnlp-ijcnlp2019.org/program/tutorials/) tutorial at [EMNLP 2019](https://www.emnlp-ijcnlp2019.org/) and [CODS-COMAD 2020](https://cods-comad.in/).
Slides can be downloaded from [here](https://shikhar-vashishth.github.io/assets/pdf/emnlp19_tutorial.pdf).
<img align="right" src="./graph.jpeg">
### Dependencies
- Compatible with PyTorch 1.x, TensorFlow 1.x and Python 3.x.
- Dependencies can be installed using `requirements.txt`.
### TensorFlow Examples:
* `tf_gcn.py` contains simplified implementation of first-order approximation of GCN model proposed by [Kipf et. al. (2016)](https://arxiv.org/abs/1609.02907)
* Extensions of the same implementation for different problems:
* Relation Extraction: [RESIDE](https://github.com/malllabiisc/RESIDE)
* GCNs for Word Embeddings: [WordGCN](https://github.com/malllabiisc/WordGCN)
* Document Time-stamping: [NeuralDater](https://github.com/malllabiisc/NeuralDater)
### PyTorch Examples:
* `pytorch_gcn.py` is pytorch equivalent of `tf_gcn.py` implemented using [pytorch-geometric](https://github.com/rusty1s/pytorch_geometric).
* Several other examples are available [here](https://github.com/rusty1s/pytorch_geometric/tree/master/examples).
### Additional Resources:
* Short writeup on theory behind Graph Convolutional Networks [[Pdf]](https://arxiv.org/abs/1911.03042) (refer Chapter-2).
* [GNN recent papers](https://github.com/naganandy/graph-based-deep-learning-literature).
### Citation:
```bibtex
@inproceedings{vashishth-etal-2019-graph,
title = "Graph-based Deep Learning in Natural Language Processing",
author = "Vashishth, Shikhar and
Yadati, Naganand and
Talukdar, Partha",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
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.",
}
```
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matched fp:e78238d1fbf339c3, topic:deep-learning, topic:pytorch, topic:tensorflow
matched fp:e78238d1fbf339c3, topic:tutorial, desc:tutorial, readme:tutorial