Code for negation detection in Dutch clinical texts.
Publication: https://doi.org/10.48550/arxiv.2209.00470
negation-detection
└───bilstm : biLSTM method code
└───data : input data (not git tracked)
└───models : model output files (not git tracked)
└───results : result files
└───robbert : RobBERT method code
└───rule-based_context : Rule-based method code
└───utils : Code for general (pre-)processing
- Notebooks to reproduce results for assessed methods are in their respective folders.
- Notebooks for general evaluation, error analysis and error comparisons are in the root directory.
We place the finetuned BERT-models on the Huggingface modelhub.
If you find any of the code published in this repository useful please cite as:
@software{bram_van_es_2022_6980076,
author = {Bram van Es and
Leon C. Reteig and
Sander C. Tan and
Marijn Schraagen and
Myrthe M. Hemker and
Sebastiaan R.S. Arends and
Miguel Rios and
Saskia Haitjema},
title = {{Negation detection using various rule-
based/machine learning methods}},
month = aug,
year = 2022,
publisher = {Zenodo},
version = 1,
doi = {10.5281/zenodo.6980076},
url = {https://doi.org/10.5281/zenodo.6980076}
}
and the associated publication
@misc{https://doi.org/10.48550/arxiv.2209.00470,
doi = {10.48550/ARXIV.2209.00470},
url = {https://arxiv.org/abs/2209.00470},
author = {van Es, Bram and Reteig, Leon C. and Tan, Sander C. and Schraagen, Marijn and Hemker, Myrthe M. and Arends, Sebastiaan R. S. and Rios, Miguel A. R. and Haitjema, Saskia},
keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; J.3; H.3.3, 68T50, 68P20},
title = {Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}