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Use this software at your own risk.

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Conceptual grounding constraints for truly robust biomedical name representations

This directory contains source code for the following paper:

Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations.
Pieter Fivez, Simon Šuster and Walter Daelemans. EACL, 2021.

If you use this code, please cite:

@inproceedings{fivez-etal-2021-conceptual,
title = "Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations",
author = "Fivez, Pieter  and
  Suster, Simon  and
  Daelemans, Walter",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
year = "2021",
publisher = "Association for Computational Linguistics",
pages = "2440--2450"}

License

GPL-3.0

Requirements

All requirements are listed in requirements.txt.

You can run pip install -r requirements.txt, preferably in a virtual environment.

The fastText model used in the paper can be downloaded from the following link: https://drive.google.com/file/d/1B07lc3eeW_zughHguugLBR4iJYQj_Wxz/view?usp=sharing
Our example scripts require a path to this downloaded model.

Data

For convenience, we only provide our adaptation of the openly available MedMentions corpus.
The source files for this corpus can be found at https://github.com/chanzuckerberg/MedMentions.

The script data/extract_medmentions.py has used these source files to create data/medmentions.json.

Code

We provide 2 scripts to run experiments from the paper.

main_dan.py trains and evaluates the DAN encoder on data/medmentions.json.
Please run python main_dan.py --help to see the options, or check the script.
The default parameters are the best parameters reported in our paper.

main_bne.py trains and evaluates our counterpart implementation of the BNE encoder, as described in:

@inproceedings{phan-etal-2019-robust,
title = "Robust Representation Learning of Biomedical Names",
author = "Phan, Minh C.  and
  Sun, Aixin  and
  Tay, Yi",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
year = "2019",
publisher = "Association for Computational Linguistics",
pages = "3275--3285"}

Please run python main_bne.py --help to see the options, or check the script.
The default parameters are the best parameters reported in our paper.