awesome-relation-extraction

Why we included this project

Relation extraction is the NLP task of identifying semantic relationships between entities in text, and this list is a solid entry point for anyone learning it. The maintainer groups resources by approach, so supervised methods, distant supervision, graph neural networks, transformer-based language models, few-shot learning, and knowledge-graph techniques each get their own section. That structure lets you follow a research thread instead of hopping between unrelated links. The datasets and frameworks sections are especially useful when you are scoping a project and want to know what labeled corpora and reusable codebases already exist before building your own. Surveys and video lectures round it out, so newcomers can move from fundamentals to current trends without hunting for the right papers. It is a curated index rather than runnable software, so its value is in the selection and organization.