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Computer Science > Computation and Language

arXiv:2201.03335 (cs)
[Submitted on 10 Jan 2022 (v1), last revised 18 Sep 2023 (this version, v6)]

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

Authors:Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Peng Wang, Wen Zhang, Zhenru Zhang, Chuanqi Tan, Qiang Chen, Feiyu Xiong, Fei Huang, Guozhou Zheng, Huajun Chen
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Abstract: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 this https URL with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in this http URL for real-time extraction of various tasks, and a demo video.
Comments: Accepted by EMNLP 2022 System Demonstrations and the project website is this http URL
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
Cite as: arXiv:2201.03335 [cs.CL]
  (or arXiv:2201.03335v6 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2201.03335

Submission history

From: Ningyu Zhang [view email]
[v1] Mon, 10 Jan 2022 13:29:05 UTC (1,654 KB)
[v2] Mon, 24 Jan 2022 02:56:19 UTC (1,654 KB)
[v3] Tue, 2 Aug 2022 12:14:23 UTC (2,356 KB)
[v4] Wed, 3 Aug 2022 00:48:23 UTC (2,356 KB)
[v5] Tue, 11 Oct 2022 11:42:24 UTC (5,011 KB)
[v6] Mon, 18 Sep 2023 16:42:06 UTC (5,011 KB)
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