Papers with Code 아카이브
Zero-shot Named Entity Recognition (NER)
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 10
Named Entity Recognition is Zero-shot Settings. The model has not been trained in the specific dataset
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data
ReverseNER: A Self-Generated Example-Driven Framework for Zero-Shot Named Entity Recognition with Large Language Models
SandboxAQ's submission to MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval
Rethinking Negative Instances for Generative Named Entity Recognition
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
PromptNER: Prompting For Named Entity Recognition
InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.