SAM-NER: Semantic Archetype Mediation for Zero-Shot Named Entity Recognition
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 \emph{Semantic Archetype Mediation} that stabilizes cross-domain transfer through an intermediate, domain-invariant archetype space. SAM-NER: (i) performs \emph{Entity Discovery} via cooperative extraction and consensus-based denoising to obt...
| Authors | Ruichu Cai, Juntao Gan, Miao Mai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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