GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction

TL;DR

GLiNER-Relex is a unified transformer-based model that jointly performs named entity recognition and relation extraction, enabling zero-shot extraction of arbitrary types and providing an efficient, open-source solution.

Contribution

It introduces a single model architecture that combines NER and RE with zero-shot capabilities, extending the GLiNER framework for improved efficiency and flexibility.

Findings

Achieves competitive performance on four standard RE benchmarks.

Supports zero-shot extraction of arbitrary entity and relation types.

Provides an open-source package with a simple inference API.

Abstract

Joint named entity recognition (NER) and relation extraction (RE) is a fundamental task in natural language processing for constructing knowledge graphs from unstructured text. While recent approaches treat NER and RE as separate tasks requiring distinct models, we introduce GLiNER-Relex, a unified architecture that extends the GLiNER framework to perform both entity recognition and relation extraction in a single model. Our approach leverages a shared bidirectional transformer encoder to jointly represent text, entity type labels, and relation type labels, enabling zero-shot extraction of arbitrary entity and relation types specified at inference time. GLiNER-Relex constructs entity pair representations from recognized spans and scores them against relation type embeddings using a dedicated relation scoring module. We evaluate our model on four standard relation extraction benchmarks: CoNLL04, DocRED, FewRel, and CrossRE, and demonstrate competitive performance against both specialized relation extraction models and large language models, while maintaining the computational efficiency characteristic of the GLiNER family. The model is released as an open-source Python package with a simple inference API that allows users to specify arbitrary entity and relation type labels at inference time and obtain both entities and relation triplets in a single call. All models and code are publicly available.