Augmenting NER Datasets with LLMs: Towards Automated and Refined Annotation

arXiv:2404.01334v1 Announce Type: cross
Abstract: In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) is recognized as a critical technology, employed across a wide array of applications. Traditional methodologies for annotating datasets for NER models are challenged by high costs and variations in dataset quality. This research introduces a novel hybrid annotation approach that synergizes human effort with the capabilities of Large Language Models (LLMs). This approach not only aims to ameliorate the noise inherent in manual …

abstract annotation applications array arxiv automated costs cs.cl cs.lg dataset datasets language language processing llms natural natural language natural language processing ner nlp processing quality recognition research technology type