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Few-shot NER
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Few-Shot Named Entity Recognition (NER) is the task of recognising a 'named entity' like a person, organization, time and so on in a piece of text e.g. "Alan Mathison [person] visited the Turing Institute [organization] in June [time].
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selection
CLLMFS: A Contrastive Learning enhanced Large Language Model Framework for Few-Shot Named Entity Recognition
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-shot Named Entity Recognition
llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models
A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition
Hybrid Multi-stage Decoding for Few-shot NER with Entity-aware Contrastive Learning
LLMs in Biomedicine: A study on clinical Named Entity Recognition
Few-shot Named Entity Recognition via Superposition Concept Discrimination
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition
Designing Informative Metrics for Few-Shot Example Selection
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation Purification
HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain
TKDP: Threefold Knowledge-enriched Deep Prompt Tuning for Few-shot Named Entity Recognition
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER
PromptNER: Prompting For Named Entity Recognition
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation
Learning In-context Learning for Named Entity Recognition
VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna
From Zero to Hero: Harnessing Transformers for Biomedical Named Entity Recognition in Zero- and Few-shot Contexts
Causal Interventions-based Few-Shot Named Entity Recognition
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
Prompt-Based Metric Learning for Few-Shot NER
Language Model Pre-Training with Sparse Latent Typing
Robustness of Demonstration-based Learning Under Limited Data Scenario
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition
SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.