基于分布校正的小样本命名实体识别方法OA

In the modern industrial sector,the perception and analysis of text data are essential for promoting intelligent manufacturing and optimizing production processes.However,industrial text data are typically characterized by high specialization,diversity,complexity,and 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.However,these methods are highly sensitive to the support set data and prone to sample-selection bias.To address this,a few-shot NER model based on Distribution Calibration called DC-NER is proposed.A two-stage approach for span detection and entity classification is adopted for the few-shot NER task.In the first stage,an entity extractor is used to select candidate entities.In the second stage,a type discriminator assigns these entities to predefined categories.To address the challenge of capturing the true class distribution with a small number of labeled samples in few-shot NER,which leads to inaccurate class prototype calculations,a method is proposed that leverages useful information from the source domain data to calibrate the class distribution in the target domain.Thus,more samples are generated based on the refined class distribution to construct more accurate prototypes,thereby improving the performance in few-shot NER tasks.Experiments on both the in-domain dataset Few-NERD and the cross-domain dataset Cross-NER reveal that DC-NER significantly outperforms the comparative models in terms of F1 value,validating its effectiveness for few-shot NER.