@inproceedings{fu-zhang-2026-recot,
    title = "{R}e{C}o{T}-{NER}: Enhancing Zero-Shot Named Entity Recognition through Chain-of-Thought Prompting and Recall-Oriented Loss Optimization",
    author = "Fu, Dabin  and
      Zhang, Fanghong",
    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://preview.aclanthology.org/ingest-acl/2026.findings-acl.227/",
    pages = "4645--4656",
    ISBN = "979-8-89176-395-1",
    abstract = "Named Entity Recognition (NER) plays a fundamental role in information extraction and domain knowledge construction. However, in specialized domains such as wind power fault diagnosis, the scarcity of labeled data makes supervised approaches impractical. Zero-shot NER provides a promising alternative but still struggles with incomplete entity detection and unstable generation boundaries. To address these challenges, we propose ReCoT-NER, a reasoning-enhanced generative framework that integrates Chain-of-Thought (CoT) prompting and recall-oriented loss optimization. The proposed CoT instruction design explicitly decomposes NER into two reasoning stages: entity span detection and entity type classification. This enables the model to follow a structured inference process. In addition, we introduce a recall-oriented loss function that reweights entity and non-entity tokens to mitigate false negatives, encouraging more inclusive entity coverage. Experiments on CrossNER, MIT, and a newly constructed wind-power NER dataset demonstrate that ReCoT-NER consistently improves recall and overall F1 performance across both general and industrial domains. Notably, ReCoT-NER achieves competitive results with just a 77M-parameter model, making it well-suited for low-resource zero-shot settings. The code for our method is publicly available at https://github.com/10637409100/RECOTNER."
}