Annotated Dataset Creation through General Purpose Language Models for non-English Medical NLP
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