DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Xin Xie, Xiang Chen, Zhoubo Li, Lei li, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Zhenru Zhang, Chuanqi Tan, Fei Huang, Guozhou Zheng, Huajun Chen

We present a new open-source and extensible knowledge extraction toolkit, called DeepKE (Deep learning based Knowledge Extraction), supporting standard fully supervised, low-resource few-shot and document-level scenarios. 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 texts 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. Besides, we present an online platform in \url{http://deepke.zjukg.cn/} for real-time extraction of various tasks. DeepKE has been equipped with Google Colab tutorials and comprehensive documents for beginners. We release the source code at \url{https://github.com/zjunlp/DeepKE}, with a demo video.

Knowledge Graph

arrow_drop_up

Comments

Sign up or login to leave a comment