yanzhh / HGERE

Source Code for "Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks"

HGERE

Source Code for "Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks" of EMNLP2023

Our code is based on PL-marker, thanks for their great work!

Setup

Install Dependencies/Prepare the datasets

Please refer to PL-marker for dependency installation and dataset preparing instructions.

Download Pre-trained Language Models

Download scibert_scivocab_uncased, bert-base-uncased and albert-xxlarge-v1 from huggingface, and place them in fold "pretrained_models".

Training

Pruners

You can use run_pruner.py to train an entity pruner model, we train with five seeds and use the pruner model with highest dev recall for downstream process.

Please run scripts in the fold: shells/pruner, for example:

bash shells/pruner/scierc/run_train_pruner_scierc.sh
...

ERE models

Then we can train the ERE models with ent_pred_{train/dev/test}.json files in the output directory of the span pruner.

For example we can train the HGERE models of scierc dataset with command as follow:

bash shells/hgere/scierc/run_train_scierc_scibert_hgere.sh

All the scripts are in the fold: shells/hgere

Evaluate the pre-trained ERE models

You can replace "--do_train --do_eval" in the scripts for training with "--do_test" if you need to evaluate on a pre-trained ERE model.

Citation

If you use our code in your research, please cite our work:

@misc{yan2023joint,
      title={Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks}, 
      author={Zhaohui Yan and Songlin Yang and Wei Liu and Kewei Tu},
      year={2023},
      eprint={2310.17238},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}