Selecting Optimal Context Sentences for Event-Event Relation Extraction

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This repo contains our PyTorch implementation for the paper Selecting Optimal Context Sentences for Event-Event Relation Extraction.

SCS-EERE

1. Create and activate a new environment:

docker build -t hieumdt/ie_env -f information-extraction-env.dockerfile .

2. Prepare data
   Download the desired corpus (HiEve, MATRES, TDD) and put it in folder .\datasets
   Download numberbatch w2v and move it to folder .\datasets

wget https://conceptnet.s3.amazonaws.com/downloads/2019/numberbatch/numberbatch-en-19.08.txt.gz
gunzip numberbatch-en-19.08.txt.gz
mv numberbatch-en-19.08.txt ./datasets

3. Train model:

python main.py --seed <your_seed> --dataset <datataset> --roberta_type <roberta_type> --best_path <path_to_save_model> --log_file <log> --bs <batch_size>

• dataset chooses from HiEve, MATRES, TDD_man, TDD_auto
• roberta_type chooses from roberta_base, roberta_large

4. Example commands: Training HiEve

python main.py --seed 1741 --dataset HiEve --roberta_type roberta_large --best_path /rst_HiEve/ --log_file HiEve_result.txt --bs 16

Note
Need to ensure the minimal number of sentences of the doc is always larger than the number of selected sentence.

License

All work contained in this package is licensed under the Apache License, Version 2.0.

Reference

Bibtex:

@article{trong2022selecting,
  title={Selecting Optimal Context Sentences for Event-Event Relation Extraction},
  author={Trong, Hieu Man Duc and Trung, Nghia Ngo and Van Ngo, Linh and Nguyen, Thien Huu},
  year={2022}
}

Email: hieuman2708@gmail.com; v.hieumdt@vinai.io