Integrating higher-level semantics into robust biomedical name representations

This directory contains source code for the following paper:

Scalable Few-Shot Learning of Robust Biomedical Name Representations.
Pieter Fivez, Simon Šuster and Walter Daelemans. BioNLP (NAACL), 2021.

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 aren't allowed to publicly share SNOMED-CT data, we demonstrate our code using only the ICD-10 data. We have extracted this data using source files which can be found at https://github.com/kamillamagna/ICD-10-CSV.

The script data/icd_chapters.py has used these source files to create data/icd10.json.

Code

We provide a script to run our training objectives from the paper using fastText embeddings as input.

main_dan.py trains our proposed encoder on data/icd10.json and reports results on the benchmarks in data/benchmarks.json
Please run python main.py --help to see the options, or check the script.
The default parameters are the best parameters reported in our paper.