Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP
This addresses the lack of task-specific datasets and models for non-English medical NLP, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of creating annotated datasets for non-English medical NLP by leveraging pretrained language models to generate training data, resulting in a publicly available German medical NER dataset and model called GPTNERMED.
Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processsing (NLP). In general, developing and applying new NLP pipelines in domain-specific contexts for tasks often requires custom designed datasets to address NLP tasks in supervised machine learning fashion. When operating in non-English languages for medical data processing, this exposes several minor and major, interconnected problems such as lack of task-matching datasets as well as task-specific pre-trained models. In our work we suggest to leverage pretrained language models for training data acquisition in order to retrieve sufficiently large datasets for training smaller and more efficient models for use-case specific tasks. To demonstrate the effectiveness of your approach, we create a custom dataset which we use to train a medical NER model for German texts, GPTNERMED, yet our method remains language-independent in principle. Our obtained dataset as well as our pre-trained models are publicly available at: https://github.com/frankkramer-lab/GPTNERMED