To install the dependency packages, run
conda create --name query_extract_EE python=3.8
conda activate query_extract_EE
pip install -r requirements.txt
- Follow https://github.com/wilburOne/ACE_ERE_Scripts to process the raw data and save to ./data/ace_en/processed_data and ./data/ere_en/processed_data respectively
- Save the event data into .txt files, process the .txt file and save as Torch TensorDataset
./setup.sh
To train the trigger detection model, run
python scripts/run_trigger_detection.py --tr_dataset=${PATH_TO_TRAIN_DATASET} --dev_dataset=${PATH_TO_DEV_DATASET}
To evaluate the trigger detection performance, run
python scripts/run_trigger_detection.py --EPOCH=0 --te_dataset=${PATH_TO_TEST_DATASET}
To train the argument detection model, run
python scripts/run_argument_detection.py --train_file_pt=${PATH_TO_TRAIN_FILE} --dev_file_pt=${PATH_TO_DEV_FILE}
To evaluate the argument detection model, run
python scripts/eval.py
If you find this repo useful, please cite the following paper:
@inproceedings{wang2022query,
title={Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding},
author={Wang, Sijia and Yu, Mo and Chang, Shiyu and Sun, Lichao and Huang, Lifu},
booktitle={Findings of the 2022 Association for Computational Linguistics},
year={2022}
}
Distributed under the MIT License.