2021/12/16 by Hyunjae Kim, Kim, Hyunjae, Jaehyo Yoo +7
Computer Science · Health Professions · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Interpreting and Communication in Healthcare #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2112.08808
openalex publication_date 2021/12/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.