Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities. This research introduces an ask-to-generate approach that automatically generates NER datasets by asking questions in simple natural language to an open-domain question answering system (e.g., "Which disease?"). Despite using fewer in-domain resources, our models, solely trained on the generated datasets, largely outperform strong low-resource models by an average F1 score of 19.4 for six popular NER benchmarks. Furthermore, our models provide competitive performance with rich-resource models that additionally leverage in-domain dictionaries provided by domain experts. In few-shot NER, we outperform the previous best model by an F1 score of 5.2 on three benchmarks and achieve new state-of-the-art performance.
@article{arxiv.2112.08808,
title = {Simple Questions Generate Named Entity Recognition Datasets},
author = {Hyunjae Kim and Jaehyo Yoo and Seunghyun Yoon and Jinhyuk Lee and Jaewoo Kang},
journal= {arXiv preprint arXiv:2112.08808},
year = {2022}
}
Comments
EMNLP 2022. Code and datasets available at https://github.com/dmis-lab/GeNER