An Intelligent Question and Answering System for Dental Healthcare

Yan Jiang; Yueshen Xu; Jin Guo; Yaning Liu; Rui Li

Broadband Communications, Networks, and Systems. 10th EAI International Conference, Broadnets 2019, Xi’an, China, October 27-28, 2019, Proceedings

Research Article

An Intelligent Question and Answering System for Dental Healthcare

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@INPROCEEDINGS{10.1007/978-3-030-36442-7_13,
    author={Yan Jiang and Yueshen Xu and Jin Guo and Yaning Liu and Rui Li},
    title={An Intelligent Question and Answering System for Dental Healthcare},
    proceedings={Broadband Communications, Networks, and Systems. 10th EAI International Conference, Broadnets 2019, Xi’an, China, October 27-28, 2019, Proceedings},
    proceedings_a={BROADNETS},
    year={2019},
    month={12},
    keywords={Question and answering Word2vec Skip-gram Bi-LSTM Attention mechanism Part-of-speech},
    doi={10.1007/978-3-030-36442-7_13}
}

Yan Jiang
Yueshen Xu
Jin Guo
Yaning Liu
Rui Li
Year: 2019
An Intelligent Question and Answering System for Dental Healthcare
BROADNETS
Springer
DOI: 10.1007/978-3-030-36442-7_13

Yan Jiang¹^,*, Yueshen Xu¹^,*, Jin Guo¹^,*, Yaning Liu¹^,*, Rui Li¹^,*

1: Xidian University

*Contact email: yjiang_2@stu.xidian.edu.cn, ysxu@xidian.edu.cn, jguo_2@stu.xidian.edu.cn, ynliu1@stu.xidian.edu.cn, rli@xidian.edu.cn

Abstract

The intelligent question and answering system is an artificial intelligence product that combines natural language processing technology and information retrieval technology. This paper designs and implements a retrieval-based intelligent question and answering system for closed domain, and focuses on researching and improving related algorithms. The intelligent question and answering system mainly includes three modules: classifier, Q&A system and Chatbots API. This paper focuses on the classifier module, and designs and implements a classifier based on neural network technology, mainly involving word vector, bidirectional long short-term memory (Bi-LSTM), and attention mechanism. The word vector technology is derived from the word2vec tool proposed by Google in 2013. This paper uses the skip-gram model in word2vec.The Q&A system mainly consists of two modules: semantic analysis and retrieval. The semantic analysis mainly includes techniques such as part-of-speech tagging and dependency parsing. The retrieval mainly relates to technologies such as indexing and search. The Chatbots API calls the API provided by Turing Robotics. The intelligent question and answering system designed and implemented in this paper has been put into use, and the user experience is very good.

Keywords: Question and answering Word2vec Skip-gram Bi-LSTM Attention mechanism Part-of-speech

Published: 2019-12-02
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-030-36442-7_13