摘要： 在现代工业领域，文本数据的感知和分析已成为推动智能制造和优化生产流程的重要手段。然而，工业文本数据通常具有高专业性、多样性和复杂性等特点，且标注成本较高，因此传统的大规模标注方法难以适用。现有的小样本命名实体识别方法多采用原型网络对实体进行分类，其中原型为属于同一类别的所有样本特征的平均值。然而，这类方法对于支持集数据的敏感性较强，容易出现样本选择性偏差的问题。因此，本文提出了基于分布矫正的小样本命名实体识别模型—DC-NER（Distribution Calibration-based Named Entity Recognition），该模型创新性地将任务分解为跨度检测和实体分类两个阶段。在实体分类阶段，通过精准的距离度量函数识别源域与目标域间的相似类别，并据此矫正目标域样本分布，生成更准确的类别原型。在同领域数据集（Few-NERD）和跨领域数据集（Cross-NER）上的实验结果表明，DC-NER在F1分数上显著优于对比模型，验证了其在小样本命名实体识别中的有效性。

Abstract: In the modern industrial sector, the perception and analysis of text data have become essential for promoting intelligent manufacturing and optimizing production processes. However, industrial text data is typically characterized by high specialization, diversity, and complexity, along with high annotation costs, making traditional large-scale annotation methods unsuitable. Existing few-shot named entity recognition(NER) methods often use prototypical networks to classify entities, where the prototype is the average of the features of all samples belonging to the same category. These methods, however, are highly sensitive to support set data and prone to sample selection bias. To address this, we propose a few-shot named entity recognition model based on distribution calibration—DC-NER（Distribution Calibration-based Named Entity Recognition）. The model innovatively decomposes the task into two phases: span detection and entity classification. During the entity classification phase, a precise distance measurement function is employed to identify similar categories between the source domain and the target domain. Based on this, the distribution of samples in the target domain is corrected to generate more accurate class prototypes. Experimental results on both in-domain dataset (Few-NERD) and cross-domain dataset (Cross-NER) demonstrate that DC-NER significantly outperforms comparative models in terms of F1 score, validating its effectiveness in few-shot named entity recognition.