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

2026-05-11

AI Summary

This work addresses the limitations of traditional approaches to named entity recognition (NER) and relation extraction (RE), which typically rely on separate models and struggle to support zero-shot joint inference over arbitrary entity and relation types. The authors propose GLiNER-Relex, the first extension of GLiNER to unified NER and RE, introducing an end-to-end framework built upon a shared bidirectional Transformer encoder. By leveraging span-level entity representations, configurable relation type embeddings, and a dedicated relation scoring module, GLiNER-Relex enables single-model, zero-shot extraction of knowledge triples with arbitrary labels. Evaluated on CoNLL04, DocRED, FewRel, and CrossRE benchmarks, it matches or surpasses both task-specific models and large language models in performance while maintaining high computational efficiency, and includes a clean, open-source 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.

Problem

• Named Entity Recognition
• Relation Extraction
• Joint Modeling
• Knowledge Graph Construction
• Unstructured Text

Innovation

• unified framework
• zero-shot relation extraction
• joint NER and RE
• relation scoring module
• bidirectional transformer encoder