Classification of Health Questions Based on Vector Extension of Keywords
Tang Xiaobo1,2,Gao Hexuan1()
1School of Information Management, Wuhan University, Wuhan 430072, China 2Center for Studies of Information Systems, Wuhan University, Wuhan 430072, China
[Objective] This paper proposes a classification model for health questions based on keywords vector expansion, aiming to improve the user experience of medical question-answering community.[Methods] First, we extracted keywords from the questions using TF-IDF and LDA models.Then, we extended the word vector features with Word2Vec and applied them to the classification of health questions.[Results] The proposed method yielded better classification results with the TF-IDF as keyword extraction method and the complete questions/answers as training corpus. The number of words in the reserved dictionary was 600, and the language model was CBOW. The values of our optimal model’s P, R, F were 0.987 2, 0.972 5 and 0.979 8 respectively.[Limitations] We did not extracted keywords of short medical texts with semantic depth.[Conclusions] Our new classification model has better performance than the existing ones.
唐晓波,高和璇. 基于关键词词向量特征扩展的健康问句分类研究 *[J]. 数据分析与知识发现, 2020, 4(7): 66-75.
Tang Xiaobo,Gao Hexuan. Classification of Health Questions Based on Vector Extension of Keywords. Data Analysis and Knowledge Discovery, 2020, 4(7): 66-75.
( The “Internet+Medical and Health Convenience and Benefited Activities” Printed and Distributed by National Health Commission[EB/OL]. [ 2018- 08- 12]. http://gcs.satcm.gov.cn/zhengcewenjian/2018-07-18/7410.html
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