We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population. DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction. With a unified framework, DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements. Specifically, DeepKE not only provides various functional modules and model implementation for different tasks and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility. We release the source code at GitHub in https://github.com/zjunlp/DeepKE with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various tasks, and a demo video.
@article{arxiv.2201.03335,
title = {DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population},
author = {Ningyu Zhang and Xin Xu and Liankuan Tao and Haiyang Yu and Hongbin Ye and Shuofei Qiao and Xin Xie and Xiang Chen and Zhoubo Li and Lei Li and Xiaozhuan Liang and Yunzhi Yao and Shumin Deng and Peng Wang and Wen Zhang and Zhenru Zhang and Chuanqi Tan and Qiang Chen and Feiyu Xiong and Fei Huang and Guozhou Zheng and Huajun Chen},
journal= {arXiv preprint arXiv:2201.03335},
year = {2023}
}
Comments
Accepted by EMNLP 2022 System Demonstrations and the project website is http://deepke.zjukg.cn/