基于判别性表示与自适应校准推理的跨域少样本命名实体识别

doi:10.16451/j.cnki.issn1003-6059.202602002

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Abstract To address the challenges of boundary ambiguity and error accumulation caused by feature distribution shifts between source and target domains in few-shot Named Entity Recognition(NER), a model of cross-domain few-shot NER via discriminative representation and adaptive calibrated inference(DR-ACI) is proposed. First, the span detection space is reshaped through an asymmetric boundary contrastive(ABC) loss. An entity-centric asymmetric constraint strategy is adopted. With this strategy, entity boundaries are explicitly sharpened while the semantic diversity of the background is preserved. Simultaneously, an adaptive gated enhancement(AGE) module is introduced to dynamically calibrate sparse prototypes through multi-level semantic fusion, thereby mitigating representation uncertainty and bias resulting from support set sparsity. Subsequently, a scenario-aware adaptive calibrated inference mechanism is designed to tackle the bottlenecks of feature norm drift and support set bias. By leveraging feature normalization and a reliability-aware dual-mode gated strategy, the above mechanism dynamically reconstructs decision boundaries to suppress transfer noise. Experimental results demonstrate that DR-ACI maintains competitive performance on Few-NERD dataset and is superior to the baseline models on cross-domain datasets. These results verify the effectiveness of the synergistic optimization of discriminative representation and adaptive inference.

Key words： Few-Shot Named Entity Recognition Discriminative Representation Adaptive Inference Prototype Calibration

Received: 28 January 2026

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.62266023,62466028), Natural Science Foundation of Jiangxi Province(No.20242BAB20045), Postgraduate Innovation Fund Project of Educational Department of Jiangxi Province(No.YJS2025068), and Jiangxi Provincial Management Science Foundation(No.20252BAA100062)

Corresponding Authors: HUANG Qi, Ph.D., associate professor. His research interests include social network analysis, rumor detection, graph neural networks, natural language processing and information retrie-val.

About author:: QIU Quanan, Master student. His research interests include natural language processing and few-shot named entity recognition.TONG Zirong, Master student. His research interests include natural language processing and few-shot named entity recognition. LUO Wenbing, Ph.D., senior experimen-ter. His research interests include natural language processing,information retrieval and knowledge graph. YI Jie, Ph.D. candidate. Her research interests include natural language processing, information management and information systems.WANG Mingwen, Ph.D., professor. His research interests include natural language processing, information extraction, information retrieval and data mining.

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Cite this article:

QIU Quanan,HUANG Qi,TONG Zirong等. Discriminative Representation and Adaptive Calibrated Inference for Cross-Domain Few-Shot Named Entity Recognition[J]. Pattern Recognition and Artificial Intelligence, 2026, 39(2): 112-126.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202602002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2026/V39/I2/112

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