Using LFtext-TextCNN to classify short text of TCM symptoms
Pages 43 - 47
Abstract
TCM (Traditional Chinese Medicine) symptoms are divided into six categories, and these symptom categories contain a lot of clinical diagnostic information. At present, the classification of TCM symptoms is mainly based on the experience of TCM physicians with there are many errors. The average length of the text description information of TCM symptoms is about 4, and labeling these symptom data is a very expensive task. Therefore, TCM symptom classification is a short text classification task with few samples. The current short text classification methods are difficult to effectively extract professional semantics and unique expressions in TCM symptom texts. Our method is based on LFtext-TextCNN to extract the general semantic information of TCM symptoms and the special semantic information of TCM, which are fused to linear network to classify TCM texts. Compared with the other 7 baseline models, our method is the best in TCM symptom classification task.
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Index Terms
- Using LFtext-TextCNN to classify short text of TCM symptoms
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Published In
October 2021
593 pages
ISBN:9781450395588
DOI:10.1145/3500931
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Association for Computing Machinery
New York, NY, United States
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Published: 22 December 2021
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ISAIMS 2021
ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences
October 29 - 31, 2021
Beijing, China
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Overall Acceptance Rate 53 of 112 submissions, 47%
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