👏</p>\n","updatedAt":"2026-05-13T06:14:56.536Z","author":{"_id":"5e6a3d4ea9afd5125d9ec064","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1584020801691-noauth.jpeg","fullname":"Stefan Schweter","name":"stefan-it","type":"user","isPro":true,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":3898,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"vi","probability":0.12120208144187927},"editors":["stefan-it"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1584020801691-noauth.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.10108","authors":[{"_id":"6a0416cd86b054ce2fa40fc3","name":"Ihor Stepanov","hidden":false},{"_id":"6a0416cd86b054ce2fa40fc4","name":"Oleksandr Lukashov","hidden":false},{"_id":"6a0416cd86b054ce2fa40fc5","name":"Mykhailo Shtopko","hidden":false},{"_id":"6a0416cd86b054ce2fa40fc6","name":"Vivek Kalyanarangan","hidden":false}],"publishedAt":"2026-05-11T00:00:00.000Z","submittedOnDailyAt":"2026-05-13T00:00:00.000Z","title":"GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction","submittedOnDailyBy":{"_id":"5e6a3d4ea9afd5125d9ec064","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1584020801691-noauth.jpeg","isPro":true,"fullname":"Stefan Schweter","user":"stefan-it","type":"user","name":"stefan-it"},"summary":"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.","upvotes":1,"discussionId":"6a0416ce86b054ce2fa40fc7","ai_summary":"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.","ai_keywords":["joint named entity recognition","relation extraction","GLiNER","bidirectional transformer encoder","zero-shot extraction","entity pair representations","relation scoring module","CoNLL04","DocRED","FewRel","CrossRE"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"5e6a3d4ea9afd5125d9ec064","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1584020801691-noauth.jpeg","isPro":true,"fullname":"Stefan Schweter","user":"stefan-it","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.10108.md"}">
GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction
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.
AI-generated summary
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.
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Cite arxiv.org/abs/2605.10108 in a model README.md to link it from this page.
Cite arxiv.org/abs/2605.10108 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2605.10108 in a Space README.md to link it from this page.
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