graph4nlp

Why we included this project

Dependency trees, coreference chains, and relations between entities give text a structure that plain sequence models tend to ignore. Graph4NLP packages graph neural networks and natural language processing together so you can actually use that structure: it provides complete implementations of established GNN-for-text models plus flexible interfaces for swapping in your own encoders and graph modules, all built on DGL and PyTorch. Researchers can experiment with custom architectures without wiring up the lower-level pieces, and data scientists can run benchmark NLP tasks without deep graph expertise. The library covers the whole pipeline, from data loading through model layers to applications, which makes it a practical starting point for prototyping ideas like relation extraction or text classification with explicit graph input.