URA-NER: A Unified Retrieval-Augmented Framework with Retrieval Alignment and Uncertainty Reduction for Low-Resource NER

arXiv:2609.24372 · cs.CL, cs.AI · Submitted 2026-09-21 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-21

Updated: 2026-09-21

Comments: 8 pages,3 figures, accepted at IJCNN 2026, conference WCCI 2026

Code: https://github.com/2025-NLP/NER

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Alignment — 18× in this paper · explained in 1 episode
• Named Entity Recognition — 14× in this paper
• Span Extraction — 10× in this paper
• In-Context Learning — 9× in this paper · explained in 4 episodes
• Verification — 8× in this paper · explained in 2 episodes
• F1 Score — 7× in this paper · explained in 3 episodes
• Knowledge Graph — 5× in this paper · explained in 7 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Instruction Tuning — 4× in this paper · explained in 2 episodes
• Progressive Granularity Retrieval — 4× in this paper
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Cosine Similarity — 3× in this paper · explained in 3 episodes
• Data Augmentation — 3× in this paper · explained in 7 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• In-Context Learning (ICL) — 3× in this paper · explained in 5 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Model-aware Representation Enhancement — 3× in this paper
• Named Entity Recognition (NER) — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Ontology — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• PromptNER: Prompting For Named Entity Recognition
• Retrieval-Enhanced Named Entity Recognition
• Bidirectional LSTM-CRF Models for Sequence Tagging
• Transformer-based Named Entity Recognition with Combined Data Representation
• LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
• UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition
• Leveraging Large Language Models for Enhanced NLP Task Performance through Knowledge Distillation and Optimized Training Strategies
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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