Zero-shot Named Entity Recognition (NER)
4 papers with code • 4 benchmarks • 4 datasets
Named Entity Recognition is Zero-shot Settings. The model has not been trained in the specific dataset
Most implemented papers
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training.
InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction
Large language models have unlocked strong multi-task capabilities from reading instructive prompts.
GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
In this paper, we propose GoLLIE (Guideline-following Large Language Model for IE), a model able to improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to comply with annotation guidelines.
Rethinking Negative Instances for Generative Named Entity Recognition
In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema.