Next Article in Journal
Special Issue on Polarimetric SAR Techniques and Applications
Next Article in Special Issue
An Efficient Network Coding-Based Fault-Tolerant Mechanism in WBAN for Smart Healthcare Monitoring Systems
Previous Article in Journal
The Synchrosqueezing Algorithm Based on Generalized S-transform for High-Precision Time-Frequency Analysis
Previous Article in Special Issue
Discrimination of Aortic and Pulmonary Components from the Second Heart Sound Using Respiratory Modulation and Measurement of Respiratory Split
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(8), 767; doi:10.3390/app7080767

Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs

College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
Author to whom correspondence should be addressed.
Received: 4 July 2017 / Revised: 22 July 2017 / Accepted: 26 July 2017 / Published: 28 July 2017
(This article belongs to the Special Issue Smart Healthcare)
View Full-Text   |   Download PDF [658 KB, uploaded 28 July 2017]   |  


This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-specific features of Chinese. We present an end-to-end character-level multi-scale convolutional neural framework in which character embeddings instead of word embeddings are used to avoid Chinese word segmentation in text preprocessing, and multi-scale convolutional neural networks (CNNs) are then introduced to extract contextual information from either question or answer sentences over different scales. The proposed framework can be trained with minimal human supervision and does not require any handcrafted features, rule-based patterns, or external resources. To validate our framework, we create a new text corpus, named cMedQA, by harvesting questions and answers from an online Chinese health and wellness community. The experimental results on the cMedQA dataset show that our framework significantly outperforms several strong baselines, and achieves an improvement of top-1 accuracy by up to 19%. View Full-Text
Keywords: question answer matching; medical domain; question answering; answer selection; character embeddings question answer matching; medical domain; question answering; answer selection; character embeddings

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, S.; Zhang, X.; Wang, H.; Cheng, J.; Li, P.; Ding, Z. Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs. Appl. Sci. 2017, 7, 767.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top