The code has been tested with Python 3. To install the dependencies, please run:
pip install -r requirements.txt
We use two datasets in this work:
- ADE. We conducted 10-fold cross-validation. The dataset can be downloaded from here.
- BioRelEx. The train and dev sets can be downloaded here. The test set is unreleased and can only be evaluated using CodeLab.
After downloading the datasets, please create a new folder resources and put the datasets into that folder.
Overall, the folder structure of the entire repo should look like:
...
models/
pymetamap/
resources/
--- ade/
------- ade_full.json
------- ade_split_0_test.json
------- ade_split_0_train.json
....
------- ade_split_9_test.json
------- ade_split_9_train.json
------- ade_types.json
--- biorelex/
------- train.json
------- dev.json
--- umls_embs.pkl
--- umls_rels.txt
--- umls_reltypes.txt
--- umls_semtypes.txt
--- text2graph.pkl
scorer/
.gitignore
ade_train.sh
...
Additional files in the resources folder include:
- The files
umls_rels.txt,umls_reltypes.txt, andumls_semtypes.txtcan be extracted directly from UMLS (to use UMLS, you need to request access permission). umls_embs.pklcontains the embeddings of Maldonado et al. 2019 and also the embeddings of the UMLS definition sentences. Note that some UMLS concepts may not have any definition sentence.text2graph.pklis a cache that maps each text input in the datasets into a graph structure of all the concepts and relations from UMLS that can be potentially relevant (found by MetaMap).
For training, please refer to the scripts ade_trainer.py and trainer.py. For example, to train a basic model for BioRelEx, you can simply run:
python trainer.py
Note: If you want me to send you UMLS-related files, please email me at tuanml2@illinois.edu (together with some proof that you have access to UMLS). I am not putting UMLS-related files online because of the UMLS licensing issue.
There are some redundant code in this repo. I am going to remove them soon.