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Joint Detection of Topic Entity and Relation for Simple Question Answering

  • Yunqi Qiu
  • Yuanzhuo Wang
  • Xiaolong Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11062)

Abstract

Knowledge Base is a machine-readable set composed of well-structured relation information between entities, and has become an essential role in automatic question answering. There are two components significant to Knowledge Base Question Answering, i.e., topic entity detection which aims to find out the entity of interest in a given question, and relation detection which aims to find out the relations relevant to the question. Traditional methods decouple these two components, ignoring the correspondence between them. In this paper, we propose a neural attention-based model, namely, Joint Detection Network, to simultaneously detect topic entities and relations for simple question answering. This model can be trained in an end-to-end manner with weak supervision. Experimental results demonstrate the effectiveness of the proposed method.

Keywords

Question answering Joint detection Knowledge base 

Notes

Acknowledgement

This work is supported by National Key Research and Development Program of China under grants 2016YFB1000902 and 2017YFC0820404, and National Natural Science Foundation of China under grants 61572469, 91646120, 61772501 and 61572473.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.CAS Key Lab of Network Data Science and Technology, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.School of Computer and Control EngineeringUniversity of Chinese Academy of SciencesBeijingChina

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