GGPONC 2.0 — The German Clinical Guideline Corpus for Oncology

This repository contains the code to reproduce results from the paper:

GGPONC 2.0 — The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers (To appear at LREC '22)

Preparation

1. Get access to GGPONC following the instructions on the project homepage and place the the contents of the 2.0 release (v2.0_2022_03_24) in the data folder
2. Install Python dependencies pip install -r requirements.txt

Preprocessing (optional)

The prepocessed, i.e., sentence-splitted and tokenized text portions, are already included in the release (data/plain_text)

If you want to do the preprocessing yourself, build the jar file from https://github.com/hpi-dhc/ggponc_preprocessing and use it with data/xml/cpg-corpus-cms.xml

Notebooks

In notebooks, we provide the following Jupyter Notebooks to reproduce the results from the paper:

• 01_Statistics.ipynb
  • Corpus Statistics and IAA Calculcation with INCEpTALYTICS
• 02_NER_Baselines.ipynb
  • NER Baselines using BERT / HuggingFace Transformers
• 03_NER_Analysis.ipynb
  • Analysis of NER errors and coordination ellipses
• new: 04_spaCy_Spancat.ipynb
  • Alternative NER implementation using spaCy's SpanCategorizer feature, which can handle overlapping and nested mentions

Running NER Experiments with HuggingFace and Hydra

In experiments, we provide Hydra configurations for the different NER experiments with the best hyperparameters found through grid search. To run such an experiment, do:

• cd experiments
• python run_experiment.py -cn <experiment>.yaml cuda=<cuda devices>
  • for instance: python run_experiment.py -cn 01_ggponc_coarse_short.yaml cuda=0

If you have installed and configured Weights & Biases, it will automatically sync your runs.

To run a hyperparameter sweep, pass the optiom -m to Hydra, e.g.:

• python run_experiment.py -m -cn=01_ggponc_coarse_short.yaml cuda=0 learning_rate=1e-6,5e-6,1e-5,5e-5,1e-4 label_smoothing_factor=0.0,0.05,0.1,0.2 weight_decay=0.0,0.05,0.1

Annotation Guide

Please refer to the annotation guide for a detailed description of the entity classes and rules.

Citing GGPONC

According to the terms of use of GGPONC, contributions which are based on the corpus must cite the following publication:

• Florian Borchert, Christina Lohr, Luise Modersohn, Thomas Langer, Markus Follmann, Jan Philipp Sachs, Udo Hahn, and Matthieu-P. Schapranow. GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, 38–48. Online: Association for Computational Linguistics, 2020.

BibTeX:

@inproceedings{borchert-etal-2020-ggponc,
    title = "{GGPONC}: A Corpus of {G}erman Medical Text with Rich Metadata Based on Clinical Practice Guidelines",
    author = "Borchert, Florian  and
      Lohr, Christina  and
      Modersohn, Luise  and
      Langer, Thomas  and
      Follmann, Markus  and
      Sachs, Jan Philipp  and
      Hahn, Udo  and
      Schapranow, Matthieu-P.",
    booktitle = "Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.louhi-1.5",
    doi = "10.18653/v1/2020.louhi-1.5",
    pages = "38--48",
  }