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BDBC-KG-NLP / Qa Survey

北航大数据高精尖中心研究张日崇团队对问答系统的调研。包括知识图谱问答系统(KBQA)和文本问答系统(TextQA),每类系统分别对学术界和工业界进行调研。

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QA-Survey

GitHub license

问答系统是人工智能和自然语言处理领域中一个倍受关注并具有广泛发展前景的研究方向,其研究兴起的主要原因是人们对快速、准确地获取信息的需求。在问答系统的研发过程中,北航大数据高精尖中心研究团队重点调研了基于知识图谱的问答系统(KBQA)和基于文本的问答系统(TextQA),并整合了其现有的学术界和工业界的相关理论和技术,共包括以下四部分:

希望能为问答系统与自然语言处理领域的相关学者和研究人员提供帮助。本survey将保持定期持续更新、持续跟踪前沿技术,如有不足请大家批评指正,欢迎各位问答系统与自然语言处理研究者取用,也欢迎大家共同完善此调研。

相关成果

学术论文

  • Richong Zhang, Yue Wang, Yongyi Mao and Jinpeng Huai: Question Answering in Knowledge Bases: A Verification Assisted Model with Iterative Training. ACM Transactions on Information Systems. 37(4): 40:1-40:26 (2019)

  • Yue Wang, Richong Zhang, Cheng Xu and Yongyi Mao: The APVA-TURBO Approach To Question Answering in Knowledge Base. COLING2018 Code

专利

  • 一种基于知识图谱的问答方法
  • 一种机场服务的社区问答方法
  • 一种值机场景的多轮对话方法

应用产品

致谢

特此感谢支持数据公开与系统研发工作的北航高精尖中心及参与这项工作的各位团队成员(排名按字母顺序、不分先后):

白宇航胡志元李航宇李喣通田源张明辉张淑慧张延钊

参与贡献方式

欢迎pull requests。对于较大的更改,请先开issue以讨论您要更改的内容。

关于我们

北京市大数据科学与脑机智能高精尖创新中心

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