MRefG

Wanli Li and Tieyun Qian: "Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction", IJCNN 2021

1. Requirements

To reproduce the reported results accurately, please install the specific version of each package.

• python 3.7.10
• torch 1.7.1
• numpy 1.19.2

All data should be put into dataset/$data_name folder in a similar format as dataset/sample, with a naming convention such that (1) train-$ratio.json indicates that certain percentage of training data are used. (2) raw-$ratio.json is a part of original training data, in which we assume the labels are unknown to model.

To replicate the experiments, first prepare the required dataset as below:

• SemEval: SemEval 2010 Task 8 data (included in dataset/semeval)
• TACRED: The TAC Relation Extraction Dataset (download)
  • Put the official dataset (in JSON format) under folder dataset/tacred in a similar format like here.

Then use the scripts from utils/data_utils.py to further preprocess the data. For SemEval, the script split the original training data into two sets (labeled and unlabeled) and then separate them into multiple ratios. For TACRED, the script first perform some preprocessing to ensure the same format as SemEval.

We provide our partitioned data included in semeval path for reproducing the reported results. You can move it to dataset path for training.

The graph data we construct can be downloaded in here

Code Overview

The main entry for all models is in train_sp.py. We provide the sparse graph model.

Citation

If you find our code and datasets useful, please cite our paper.

@inproceedings{DBLP:conf/ijcnn/LiQCTZZ21,
  author    = {Wanli Li and
               Tieyun Qian and
               Xu Chen and
               Kejian Tang and
               Shaohui Zhan and
               Tao Zhan},
  title     = {Exploit a Multi-head Reference Graph for Semi-supervised Relation
               Extraction},
  booktitle = {International Joint Conference on Neural Networks, {IJCNN} 2021, Shenzhen,
               China, July 18-22, 2021},
  pages     = {1--7},
  publisher = {{IEEE}},
  year      = {2021},
}