@inproceedings{aee1c3db9397429f997175967b8a7772,
title = "Diabetes-related topic detection in Chinese health websites using deep learning",
abstract = "With 98.4 million people diagnosed with diabetes in China, most of the Chinese health websites provide diabetes related news and articles in diabetes subsection for patients. However, most of the articles are uncategorized and without a clear topic or theme, resulting in time consuming information seeking experience. To address this issue, we propose an advanced deep learning approach to detect topics for diabetes related articles from health websites. Our research framework for topic detection on diabetes related articles in Chinese is the first one to incorporate deep learning in topic detection in Chinese. It can identify topics of diabetes articles with high performance and potentially assist health information seeking. To evaluate our framework, experiment is conducted on a test bed of 12,000 articles. The results showed the framework achieved an accuracy of 70% in detecting topics and significantly outperformed the SVM based approach.",
keywords = "Chinese, classification, deep learning, diabetes, topic detection",
author = "Xinhuan Chen and Yong Zhang and Chunxiao Xing and Xiao Liu and Hsinchun Chen",
note = "Funding Information: This work is supported by Science Foundation Ireland (Grant No. 07/CE/I1142). Thanks to Yvette Graham and Sudip Naskar for proof reading, Andy Way, Khalil Sima'an, Yanjun Ma, and annonymous reviewers for comments, and Machine Translation Marathon.; 2nd International Conference for Smart Health, CSH 2014 ; Conference date: 10-07-2014 Through 11-07-2014",
year = "2014",
doi = "10.1007/978-3-319-08416-9_2",
language = "English (US)",
isbn = "9783319084152",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "13--24",
booktitle = "Smart Health - International Conference, ICSH 2014, Proceedings",
}