The Million-Label NER: Breaking Scale Barriers with GLiNER bi-encoder
This paper introduces GLiNER-bi-Encoder, a novel architecture for Named Entity Recognition (NER) that harmonizes zero-shot flexibility with industrial-scale efficiency. While the original GLiNER framework offers strong generalization, its joint-encoding approach suffers from quadratic complexity as the number of entity labels increases. Our proposed bi-encoder design decouples the process into a dedicated label encoder and a context encoder, effectively removing the context-window bottleneck. This architecture enables the simultaneous recognition of thousands, and potentially millions, of entity types with minimal overhead. Experimental results demonstrate state-of-the-art zero-shot perfo...
| Authors | Ihor Stepanov, Mykhailo Shtopko, Dmytro Vodianytskyi et al. |
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| Primary category | cs.CL |
| Published | 11 February 2026 |
| Last updated at source | 11 February 2026 |
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