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Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition

Paper · 2025 · Yuming Yang, Wantong Zhao, Caishuang Huang et al.

Summary

Open NER requires a model to find entities of any requested type in text from any domain, and even strong LLMs such as GPT-4 perform poorly at it. Fine-tuning on many existing NER corpora helps, but simply merging them causes two problems. The same label (e.g., LOC) is defined differently across datasets, and frequent entity types are heavily over-represented. The authors collect 54 English and Chinese datasets from 16 domains and clean them in two stages. First, they detect conflicting definitions with BERT-based cross-dataset validation and rule-based checks. Trained annotators then split ambiguous types and give them readable, hierarchical natural-language names. The result is a shared taxonomy of more than 400 types, released in full as B2NERD_all. Second, a pruning step samples evenly per type and prefers textually dissimilar examples, producing the compact B2NERD. LLMs tuned on this data (B2NER models) beat GPT-4 and earlier systems on out-of-domain tests. This suggests that careful curation of human annotations can outweigh raw data volume.

Key points

  • Source data: B2NERD is derived from 54 existing English and Chinese NER datasets across 16 domains. Its universal taxonomy contains 400+ entity types. B2NERD_all is described as the largest bilingual NER collection so far.
  • Conflict detection: inconsistent labels are found by training a BERT model on one dataset and testing it on others. For example, LOC is compared across ACE2005, CoNLL2003, WikiANN and OntoNotes.
  • Resolving conflicts: the authors fix differences in scope, span, mention type and granularity. Examples of new labels are "General Mentions of Person" and "Person->Others". Label naming follows four rules: readable, unambiguous, hierarchical and flexible.
  • Results: B2NER models outperform GPT-4 by 6.8–12.0 F1 on 3 out-of-domain benchmarks covering 15 datasets and 6 languages. Their gains over previous methods are 3.0% for English, 6.8% for Chinese and 6.7% in the multilingual setting.
  • Limitations and availability: current OOD benchmarks contain dataset-specific annotation conventions, as the Restaurant dataset errors show. Nested NER (ACE2005, GENIA) is handled only through dedicated labels. Data, models and code are available at github.com/UmeanNever/B2NER.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Fudan UniversityShanghai · China167 works · 20,987 citations🔬Peng Cheng LaboratoryShenzhen · China38 works · 2,504 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Vision and Pattern Recognition · 2,869 works

🧭 Topics 1Research subjects and languages the work is about.Named entity recognition · 200 works

Cite

Cite as Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang and Xuanjing Huang. 2025. Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{yang2025beyond,
  title = {{Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition}},
  author = {Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang and Xuanjing Huang},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.725/}
}

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