Liankuan Tao


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DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang | Xin Xu | Liankuan Tao | Haiyang Yu | Hongbin Ye | Shuofei Qiao | Xin Xie | Xiang Chen | Zhoubo Li | Lei Li
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

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 with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in for real-time extraction of various tasks, and a demo video.