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Convolutional Neural Network-Based Question Answering Over Knowledge Base with Type Constraint

  • Yongrui Chen
  • Huiying Li
  • Zejian Xu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 957)

Abstract

We propose a staged framework for question answering over a large-scale structured knowledge base. Following existing methods based on semantic parsing, our method relies on various components for solving different sub-tasks of the problem. In the first stage, we directly use the result of entity linking to obtain the topic entity in a question, and simplify the process as a semantic matching problem. We train a neural network to match questions and predicate sequences to get a rough set of candidate answer entities from the knowledge base. Unlike traditional methods, we also consider entity type as a constraint on candidate answers to remove wrong candidates from the rough set in the second stage. By applying a convolutional neural network model to match questions and predicate sequences and a type constraint to filter candidate answers, our method achieves an average F1 measure of 74.8% on the WEBQUESTIONSSP dataset, it is competitive with state-of-the-art semantic parsing approaches.

Keywords

Question answering Type constraint Convolutional neural network Knowledge base Semantic parsing 

Notes

Acknowledgements

The work is supported by the Natural Science Foundation of China under grant No. 61502095, and the Natural Science Foundation of Jiangsu Province under Grant BK20140643.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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