Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling (SePaL)

This package offers an implementation of SePaL. For more details, please see the paper Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling.

Framework Overview

A Self-Supervised Prototype Representation Learning (SePaL) framework is proposed for dynamic corporate profiling. By exploiting the topological information of an event graph and exploring self-supervised learning techniques, SePaL can obtain unified corporate representations that are robust to event noises and can be easily fine-tuned to benefit various down-stream applications with only a few annotated data.

Citation

If you find this repository useful in your research, please consider citing the following paper:

@inproceedings{yuan2021self,
  title={Self-Supervised Prototype Representation Learning for Event-Based Corporate Profiling},
  author={Yuan, Zixuan and Liu, Hao and Hu, Renjun and Zhang, Denghui and Xiong, Hui},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={35},
  number={5},
  pages={4644--4652},
  year={2021}
}