@inproceedings{cai-etal-2026-sam,
title = "{SAM}-{NER}: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition",
author = "Cai, Ruichu and
Gan, Juntao and
Mai, Miao and
Hao, Zhifeng and
Xu, Boyan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2050/",
pages = "41216--41231",
ISBN = "979-8-89176-395-1",
abstract = "Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large language model{'}s (LLM{'}s) intrinsic semantic organization. As a result, directly mapping entity mentions to fine-grained target labels can induce systematic semantic drift, especially when target schemas are novel or semantically overlapping. We propose \textbf{SAM-NER}, a three-stage framework based on \textit{Semantic Archetype Mediation} that stabilizes cross-domain transfer through an intermediate, domain-invariant archetype space. SAM-NER: (i) performs \textit{Entity Discovery} via cooperative extraction and consensus-based denoising to obtain high-coverage, high-fidelity entity spans; (ii) conducts \textit{Abstract Mediation} by projecting entities into a compact set of universal semantic archetypes distilled from high-level ontological abstractions; and (iii) applies \textit{Semantic Calibration} to resolve archetype-level predictions into target-domain types through constrained, definition-aligned inference with a frozen LLM. Experiments on the CrossNER benchmark show that SAM-NER consistently outperforms strong prior ZS-NER baselines in cross-domain settings."
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<abstract>Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large language model’s (LLM’s) intrinsic semantic organization. As a result, directly mapping entity mentions to fine-grained target labels can induce systematic semantic drift, especially when target schemas are novel or semantically overlapping. We propose SAM-NER, a three-stage framework based on Semantic Archetype Mediation that stabilizes cross-domain transfer through an intermediate, domain-invariant archetype space. SAM-NER: (i) performs Entity Discovery via cooperative extraction and consensus-based denoising to obtain high-coverage, high-fidelity entity spans; (ii) conducts Abstract Mediation by projecting entities into a compact set of universal semantic archetypes distilled from high-level ontological abstractions; and (iii) applies Semantic Calibration to resolve archetype-level predictions into target-domain types through constrained, definition-aligned inference with a frozen LLM. Experiments on the CrossNER benchmark show that SAM-NER consistently outperforms strong prior ZS-NER baselines in cross-domain settings.</abstract>
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%0 Conference Proceedings
%T SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition
%A Cai, Ruichu
%A Gan, Juntao
%A Mai, Miao
%A Hao, Zhifeng
%A Xu, Boyan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cai-etal-2026-sam
%X Zero-shot Named Entity Recognition (ZS-NER) remains brittle under domain and schema shifts, where unseen label definitions often misalign with a large language model’s (LLM’s) intrinsic semantic organization. As a result, directly mapping entity mentions to fine-grained target labels can induce systematic semantic drift, especially when target schemas are novel or semantically overlapping. We propose SAM-NER, a three-stage framework based on Semantic Archetype Mediation that stabilizes cross-domain transfer through an intermediate, domain-invariant archetype space. SAM-NER: (i) performs Entity Discovery via cooperative extraction and consensus-based denoising to obtain high-coverage, high-fidelity entity spans; (ii) conducts Abstract Mediation by projecting entities into a compact set of universal semantic archetypes distilled from high-level ontological abstractions; and (iii) applies Semantic Calibration to resolve archetype-level predictions into target-domain types through constrained, definition-aligned inference with a frozen LLM. Experiments on the CrossNER benchmark show that SAM-NER consistently outperforms strong prior ZS-NER baselines in cross-domain settings.
%U https://aclanthology.org/2026.findings-acl.2050/
%P 41216-41231
Markdown (Informal)
[SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition](https://aclanthology.org/2026.findings-acl.2050/) (Cai et al., Findings 2026)
ACL