WARNING: This repository is no longer maintained.

This project is archived and will not receive further updates, bug fixes, or security patches. Issues and pull requests may not be reviewed.

Use this software at your own risk.

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Integrating higher-level semantics into robust biomedical name representations

This directory contains source code for the following paper:

Integrating Higher-Level Semantics into Robust Biomedical Name Representations.
Pieter Fivez, Simon Šuster and Walter Daelemans. LOUHI (EACL), 2021.

If you use this code, please cite:

@inproceedings{fivez-etal-2021-integrating,
title = "Integrating Higher-Level Semantics into Robust Biomedical Name Representations",
author = "Fivez, Pieter  and
  Suster, Simon  and
  Daelemans, Walter",
booktitle = "Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis",
year = "2021",
publisher = "Association for Computational Linguistics",
pages = "49--58"}

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 script requires a path to this downloaded model.

Data

Since we are not allowed to share SNOMED-CT data, we demonstrate our code using the openly available MedMentions corpus.
We have used this corpus as fine-grained synonym sets in this previous publication:

@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"}

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 a script to run our training objectives 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.