Graph Deep Learning for Natural Language Processing: Methods, Models, and the Graph4NLP Library

This article presents a comprehensive overview of applying graph deep learning to natural language processing (NLP). It begins by explaining why graph structures are needed for NLP tasks and reviews traditional graph‑based NLP methods such as dependency and constituency graphs, AMR graphs, and co‑occurrence graphs.

The core of the discussion focuses on graph neural networks (GNNs). It describes the basic idea of GNNs—aggregating neighbor node embeddings—and outlines four typical graph convolution types (spectral, spatial, attention‑based, recurrent). It also explains pooling strategies for node‑level and graph‑level tasks.

Two major approaches to constructing NLP graphs are detailed: static graph construction, which relies on external linguistic knowledge (syntax, semantics, logic, co‑occurrence) to build graphs before training, and dynamic graph construction, which learns the graph structure directly from raw text via similarity matrices and attention or cosine‑based kernels.

Graph representation learning is categorized into homogeneous, multi‑relational, and heterogeneous graphs, with examples of how each is modeled using specialized GNN architectures such as R‑GCN, edge‑embedding GNNs, and meta‑path based heterogeneous GNNs.

The article then describes graph‑to‑sequence (Graph2Seq) models for NLP tasks, including encoder‑decoder frameworks that use GNN encoders and RNN or tree‑structured decoders for tasks like question generation and summarization.

Finally, the open‑source Graph4NLP Python library is introduced. Built on PyTorch, DGL, and CoreNLP with HuggingFace integration, the library provides modules for static and dynamic graph construction, various GNN models (GCN, GraphSAGE, etc.), and downstream pipelines for classification and generation tasks. Experimental results on several NLP benchmarks demonstrate its effectiveness.