Awesome

Full Abstract Relation Extraction from Biological Texts with Bi-affine Relation Attention Networks

This code was used in the paper:

Simultaneously Self-attending to All Mentions for Full-Abstract Biological Relation Extraction
Patrick Verga , Emma Strubell, and Andrew McCallum.
North American Chapter of the Association for Computational Linguistics (NAACL) 2018

Requirements

python version 2.7 tensorflow version 1.0.1

Setup Environment Variables

From this directory call: source set_environment.sh
Note: this will only set the paths for this session.

Processing Data

CDR

Process the CDR dataset
${CDR_IE_ROOT}/bin/process_CDR/process_CDR.sh

Process the CDR dataset including additional weakly labeled data
${CDR_IE_ROOT}/bin/process_CDR/process_CDR_extra_data.sh

These scripts will use byte-pair encoding (BPE) tokenization. There are also scripts to tokenize using the Genia tokenizer.

Run Model

Train a model locally on gpu id 0
${CDR_IE_ROOT}/bin/run.sh ${CDR_IE_ROOT}/configs/cdr/relex/cdr_2500 0

If you are using a cluster with Slurm, you can instead use this command:
${CDR_IE_ROOT}/bin/srun.sh ${CDR_IE_ROOT}/configs/cdr/relex/cdr_2500

Saving loading models

By default the model will be evaulated on the CDR dev set. To save the best model to the file 'model.tf', add the save_model flag
${CDR_IE_ROOT}/bin/run.sh ${CDR_IE_ROOT}/configs/cdr/relex/cdr_2500 0 --save_model model.tf

To load a saved model, run
${CDR_IE_ROOT}/bin/run.sh ${CDR_IE_ROOT}/configs/cdr/relex/cdr_2500 0 --load_model path/to/model.tf

Pretrained Models

You can download some pretrained models here

Generating the CTD dataset

This script will generate the full CTD dataset. The following command will tokenize using BPE with a budget of 50k tokens.
${CDR_IE_ROOT}/bin/process_CTD/generate_full_CTD_data.sh

You can also generate the data using the genia tokenizer with
${CDR_IE_ROOT}/bin/process_CTD/generate_full_CTD_data_genia.sh

By default, abstracts with > 500 tokens are discarded. To not filter you can change the MAX_LEN variable to a very large number.