Graph Neural Networks (GNNs) for NLP

[Cgarg9]

Description:

To explore how Graph Neural Networks (GNNs) improve NLP, add a notebook that compares GNN-based models for text tasks.

Tasks:

• Compare GCN (Graph Convolutional Network), GAT (Graph Attention Network), and HGT (Heterogeneous Graph Transformer) for tasks like relation extraction, entity linking, and document classification.
• Evaluate improvements over standard transformers using accuracy and F1-score metrics.
• Summarize when GNN-based approaches are useful for NLP.
• Name the notebook gnn_nlp_comparison.ipynb.
• Update the README file with relevant references.