ASPER: A Jupyter Notebook repository from lehgtrung

This is the official code for the paper

ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction pdf

The code is based on the following paper's code: SpERT: Span-based Entity and Relation Transformer link to github code

Setup (copy from SpERT's)

Requirements

Required
- Python 3.5+
- PyTorch (tested with version 1.4.0)
- transformers (+sentencepiece, e.g. with 'pip install transformers[sentencepiece]', tested with version 4.1.1)
- scikit-learn (tested with version 0.24.0)
- tqdm (tested with version 4.55.1)
- numpy (tested with version 1.17.4)
- clingo (version > 5.5), installation instruction
Optional
- jinja2 (tested with version 2.10.3) - if installed, used to export relation extraction examples
- tensorboardX (tested with version 1.6) - if installed, used to save training process to tensorboard
- spacy (tested with version 3.0.1) - if installed, used to tokenize sentences for prediction

Fetch data

Fetch converted (to specific JSON format) CoNLL04 [1] (we use the same split as [4]), SciERC [2] and ADE [3] datasets (see referenced papers for the original datasets):

bash ./scripts/fetch_datasets.sh

Run the code

Split data

python split_data.py <dataset> <percentage> <num_folds>

For example: we want split CoNLL04 dataset into 5 folds with 20% labeled data

python split_data.py conll04 20 5

To run ASPER

python run_ker.py --dataset <dataset> --fold <fold_number> --percent <percentage> --max_iter <max_iteration>

For example: we want to run on CoNLL04 dataset with fold #1, with 20% labeled data, maximum 5 iterations

python run_ker.py --dataset conll04 --fold 1 --percent 20 --max_iter 5

To run comparison methods

The command for running comparison methods (self/curriculum/tri-training) is similar, For example: for comparison, we want to run tri-training with fold #1, with 20% labeled data, maximum 5 iterations

python run_tri_training.py --dataset conll04 --fold 1 --percent 20 --max_iter 5

Citation

Please cite our paper as:

@article{le_cao_cao son_2023,
title={ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction},
volume={23},
DOI={10.1017/S1471068423000297},
number={4},
journal={Theory and Practice of Logic Programming},
author={LE, TRUNG HOANG and CAO, HUIPING and CAO SON, TRAN}, year={2023}, pages={765–781}}

lehgtrung/ASPER