Survey of Graph Neural Networks for Natural Language Processing

This article presents a thorough survey of Graph Neural Networks (GNNs) for Natural Language Processing (NLP), summarizing the most recent and comprehensive literature on the topic.

It begins with an overview of traditional text modeling approaches (bag‑of‑words, sequence models) and motivates the need for graph‑based representations to capture richer syntactic and semantic relationships in language.

The survey classifies GNN methods for NLP along three dimensions: graph construction, graph representation learning, and encoder‑decoder architectures. It details static graph construction techniques (dependency graphs, constituency trees, AMR, information‑extraction graphs, discourse, knowledge, coreference, similarity, co‑occurrence, topic, and application‑driven graphs) and discusses the emerging trend of dynamic graph construction that learns graph structures jointly with downstream tasks.

In the representation learning section, the paper reviews GNN fundamentals, various graph filters (spectral, spatial, attention‑based, recurrent) and pooling strategies, as well as specialized models for homogeneous, multi‑relation, and heterogeneous graphs (e.g., R‑GCN, R‑GAT, HAN, HGT). It also covers graph‑transformer hybrids that integrate structural information into self‑attention mechanisms.

The encoder‑decoder part examines Graph‑to‑Sequence, Graph‑to‑Tree, and Graph‑to‑Graph models, highlighting how GNN encoders combined with RNN/Transformer decoders improve tasks such as machine translation, summarization, structured‑data‑to‑text, question generation, and many others.

A large portion of the survey is devoted to applications, including natural language generation, machine reading comprehension, question answering, dialogue systems, text classification, matching, topic modeling, sentiment analysis, knowledge‑graph tasks, information extraction, parsing, reasoning, and semantic role labeling.

The paper concludes by summarizing challenges—such as graph construction scalability, handling heterogeneous relations, and integrating GNNs with large pretrained language models—and outlines promising future research directions.