research-article

Medical Question Similarity Calculation Based On Weighted Domain Dictionary

Authors:

Guokai Yan,

Jianqiang LiAuthors Info & Claims

ICBDC '18: Proceedings of the 3rd International Conference on Big Data and Computing

Pages 104 - 107

https://doi.org/10.1145/3220199.3220226

Published: 28 April 2018 Publication History

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Abstract

In the calculation of question similarity in the mobile medical question answering system, not all the importance of the words are the same. To solve the problem, we proposed a improved similarity calculation method based on the vector space model and combining the weighted domain dictionary for mobile health questions in question and answer system. Adding weight to field word in target question to bring a higher scores of similarity calculation, reduce the impact of non-field word in similarity calculation.

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Cited By

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Zafar AVarshney DKumar Sahoo SDas AEkbal A(2024)Are my answers medically accurate? Exploiting medical knowledge graphs for medical question answeringApplied Intelligence10.1007/s10489-024-05282-854:2(2172-2187)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s10489-024-05282-8
Huang XZhang JXu ZOu LTong J(2021)A knowledge graph based question answering method for medical domainPeerJ Computer Science10.7717/peerj-cs.6677(e667)Online publication date: 1-Sep-2021
https://doi.org/10.7717/peerj-cs.667
Zhu LDai XHuang QXiang HZheng J(2019)Topic Judgment Helps Question Similarity Prediction in Medical FAQ Dialogue Systems2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00140(966-972)Online publication date: Nov-2019
https://doi.org/10.1109/ICDMW.2019.00140

Index Terms

Medical Question Similarity Calculation Based On Weighted Domain Dictionary
1. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Unsupervised learning and clustering

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Published In

ICBDC '18: Proceedings of the 3rd International Conference on Big Data and Computing

April 2018

155 pages

ISBN:9781450364263

DOI:10.1145/3220199

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shenzhen University: Shenzhen University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 April 2018

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ICBDC '18

ICBDC '18: 2018 International Conference on Big Data and Computing

April 28 - 30, 2018

Shenzhen, China

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Cited By

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Zafar AVarshney DKumar Sahoo SDas AEkbal A(2024)Are my answers medically accurate? Exploiting medical knowledge graphs for medical question answeringApplied Intelligence10.1007/s10489-024-05282-854:2(2172-2187)Online publication date: 31-Jan-2024
https://doi.org/10.1007/s10489-024-05282-8
Huang XZhang JXu ZOu LTong J(2021)A knowledge graph based question answering method for medical domainPeerJ Computer Science10.7717/peerj-cs.6677(e667)Online publication date: 1-Sep-2021
https://doi.org/10.7717/peerj-cs.667
Zhu LDai XHuang QXiang HZheng J(2019)Topic Judgment Helps Question Similarity Prediction in Medical FAQ Dialogue Systems2019 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2019.00140(966-972)Online publication date: Nov-2019
https://doi.org/10.1109/ICDMW.2019.00140

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