GNNs-for-NLP

Graph Neural Networks for Natural Language Processing

The repository contains code examples for GNN-for-NLP tutorial at EMNLP 2019 and CODS-COMAD 2020.

Slides can be downloaded from here.

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)
• Extensions of the same implementation for different problems:
  • Relation Extraction: RESIDE
  • GCNs for Word Embeddings: WordGCN
  • Document Time-stamping: NeuralDater

PyTorch Examples:

• pytorch_gcn.py is pytorch equivalent of tf_gcn.py implemented using pytorch-geometric.
• Several other examples are available here.

Additional Resources:

• Short writeup on theory behind Graph Convolutional Networks [Pdf] (refer Chapter-2).
• GNN recent papers.

Citation:

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