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A Chinese Question Answering System for Single-Relation Factoid Questions

  • Yuxuan LaiEmail author
  • Yanyan Jia
  • Yang Lin
  • Yansong Feng
  • Dongyan Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10619)

Abstract

Aiming at the task of open domain question answering based on knowledge base in NLPCC 2017, we build a question answering system which can automatically find the promised entities and predicates for single-relation questions. After a features based entity linking component and a word vector based candidate predicates generation component, deep convolutional neural networks are used to rerank the entity-predicate pairs, and all intermediary scores are used to choose the final predicted answers. Our approach achieved the F1-score of 47.23% on test data which obtained the first place in the contest of NLPCC 2017 Shared Task 5 (KBQA sub-task). Furthermore, there are also a series of experiments which can help other developers understand the contribution of every part of our system.

Keywords

Natural language question answering Knowledge base Information extraction Deep convolutional neural network 

Notes

Acknowledgement

We would like to thank members in our NLP group and the anonymous reviewers for their helpful feedback. This work was supported by National High Technology R&D Program of China (Grant No. 2015AA015403), Natural Science Foundation of China (Grant No. 61672057, 61672058).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yuxuan Lai
    • 1
    Email author
  • Yanyan Jia
    • 1
  • Yang Lin
    • 1
  • Yansong Feng
    • 1
  • Dongyan Zhao
    • 1
  1. 1.Institute of Computer Science & TechnologyPeking UniversityBeijingChina

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