摘要
针对跨域少样本命名实体识别任务因源域特征与目标域特征分布偏移导致的边界模糊与误差累积问题,提出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模型(Discriminative Representation and Adaptive Calibrated Inference for Cross-Domain Few-Shot Named Entity Recognition,DR-ACI).首先,设计非对称边界对比损失重塑跨度检测空间,采用实体中心的非对称约束策略,在保持背景语义多样性的同时显式锐化实体边界.同时引入自适应门控增强模块,通过多层级语义融合对稀疏原型进行动态校准,降低因支持集样本稀疏带来的表征不确定性与偏差.然后,设计场景感知的自适应校准推理机制,针对特征模长漂移与支持集偏差瓶颈,利用特征归一化与可靠性感知的双模式门控策略,动态重构判决边界,抑制迁移噪声.实验表明,DR-ACI在Few-NERD数据集上具有一定的竞争力,同时在跨域数据集上性能较优,由此验证判别性表示与自适应推理协同优化的有效性.
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.
作者
邱全安
黄琪
童梓荣
罗文兵
易洁
王明文
QIU Quanan;HUANG Qi;TONG Zirong;LUO Wenbing;YI Jie;WANG Mingwen(School of Digital Industry,Jiangxi Normal University,Shang-rao 334000;School of Artificial Intelligence,Jiangxi Normal University,Nanchang 330022;Management Science and Engineering Research Center,Jiangxi Normal University,Nanchang 330022;School of Big Data,Shangrao Vocational and Technical Co-llege,Shangrao 334109)
出处
《模式识别与人工智能》
北大核心
2026年第2期112-126,共15页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.62266023,62466028)
江西省自然科学基金项目(No.20242BAB20045)
江西省教育厅研究生创新基金项目(No.YJS2025068)
江西省管理科学项目(No.20252BAA100062)资助。
关键词
少样本命名实体识别
判别性表示
自适应推理
原型校准
Few-Shot Named Entity Recognition
Discriminative Representation
Adaptive Inference
Prototype Calibration
