GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction
Summary
GLiNER-Relex is a unified framework for joint named entity recognition and relation extraction that leverages a shared transformer encoder for zero-shot capabilities. The paper demonstrates competitive performance on standard benchmarks and releases the model as an open-source Python package.
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Paper page - GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction
Source: https://huggingface.co/papers/2605.10108
Abstract
A unified model for joint named entity recognition and relation extraction that uses a shared transformer encoder to simultaneously identify entities and extract relations with zero-shot capabilities.
Joint named entity recognition(NER) andrelation 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 introduceGLiNER-Relex, a unified architecture that extends theGLiNERframework to perform both entity recognition andrelation extractionin a single model. Our approach leverages a sharedbidirectional transformer encoderto jointly represent text, entity type labels, and relation type labels, enablingzero-shot extractionof arbitrary entity and relation types specified at inference time.GLiNER-Relex constructsentity pair representationsfrom recognized spans and scores them against relation type embeddings using a dedicatedrelation scoring module. We evaluate our model on four standardrelation extractionbenchmarks:CoNLL04,DocRED,FewRel, andCrossRE, and demonstrate competitive performance against both specializedrelation extractionmodels and large language models, while maintaining the computational efficiency characteristic of theGLiNERfamily. 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.
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