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arXiv 2201.03335cs.CLcs.AIcs.IRcs.LG
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DeepKE：一个基于深度学习的知识抽取工具包，用于知识库扩充

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

Zhejiang University（浙江大学）；Alibaba Group（阿里巴巴集团）
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… 展开作者

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

备注：
Accepted by EMNLP 2022 System Demonstrations and the project website is http://deepke.zjukg.cn/
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AI总结：

本文提出开源可扩展的知识抽取工具包 DeepKE，统一支持低资源、文档级和多模态场景下的命名实体识别、关系抽取与属性抽取，并提供在线系统与教程。

摘要：

我们提出了一个开源且可扩展的知识抽取工具包 DeepKE，支持知识库扩充中复杂的低资源、文档级和多模态场景。DeepKE 实现了多种信息抽取任务，包括命名实体识别、关系抽取和属性抽取。通过统一的框架，DeepKE 允许开发者和研究人员根据自身需求定制数据集和模型，从非结构化数据中抽取信息。具体而言，DeepKE 不仅为不同任务和场景提供了各种功能模块和模型实现，还通过一致的框架组织所有组件，以保持足够的模块化和可扩展性。我们在 GitHub 的 https://github.com/zjunlp/DeepKE 上发布了源代码，并提供 Google Colab 教程和面向初学者的全面文档。此外，我们还在 http://deepke.openkg.cn/EN/re_doc_show.html 上提供了一个用于实时执行各种抽取任务的在线系统，以及一个演示视频。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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