GERNERMED++: Transfer Learning in German Medical NLP
This work provides a baseline model for the German research community in medical NLP, addressing the scarcity of open, public models for German medical entity recognition.
The authors tackled the problem of named entity recognition (NER) for German medical texts by developing GERNERMED++, an open model that outperforms its predecessor, using transfer learning, word-alignment, and neural machine translation to achieve strong results.
We present a statistical model for German medical natural language processing trained for named entity recognition (NER) as an open, publicly available model. The work serves as a refined successor to our first GERNERMED model which is substantially outperformed by our work. We demonstrate the effectiveness of combining multiple techniques in order to achieve strong results in entity recognition performance by the means of transfer-learning on pretrained deep language models (LM), word-alignment and neural machine translation. Due to the sparse situation on open, public medical entity recognition models for German texts, this work offers benefits to the German research community on medical NLP as a baseline model. Since our model is based on public English data, its weights are provided without legal restrictions on usage and distribution. The sample code and the statistical model is available at: https://github.com/frankkramer-lab/GERNERMED-pp