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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (7): 66-75    DOI: 10.11925/infotech.2096-3467.2019.1299
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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
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Abstract  

[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.

Key words： Feature Expansion      Classification of Short Texts      Word2Vec      TF-IDF     
Received: 04 December 2019      Published: 25 July 2020
ZTFLH:  TP391  
Corresponding Authors: Gao Hexuan     E-mail: gaohexuan@whu.edu.com
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Cite this article:

Tang Xiaobo,Gao Hexuan. Classification of Health Questions Based on Vector Extension of Keywords. Data Analysis and Knowledge Discovery, 2020, 4(7): 66-75.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1299     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I7/66

The Health Questions Classification Model Based on the Vector Feature Extension of Keywords
The Structure of LDA
序号 分类 数量 序号 分类 数量
1 牛皮癣 14 127 9 酒渣鼻 153
2 白癜风 20 984 10 灰指甲 686
3 荨麻疹 2 362 11 花斑癣 221
4 鱼鳞病 696 12 腋下多汗 360
5 脱发 2 515 13 银屑病 1 758
6 湿疹 2 122 14 头癣 340
7 腋臭 2 492 15 狐臭 523
8 带状疱疹 1 000
Classification and Quantity Distribution of Health Questions
Perplexity and Topic Numbers Curve
语料库 CBOW skip-gram
P R F P R F
维基百科中文语料 0.945 4 0.912 4 0.939 4 0.948 5 0.914 9 0.931 4
健康问句语料库 0.953 5 0.921 2 0.944 5 0.956 3 0.923 4 0.939 6
健康问句医生回答语料库 0.950 4 0.918 8 0.950 7 0.953 3 0.920 2 0.936 5
问答全集语料库 0.956 7 0.925 4 0.950 4 0.959 6 0.927 5 0.943 3
Classification Effects of Different Corpus
保留词典
词语数（个）		 CBOW skip-gram
P R F P R F
300 0.985 6 0.966 9 0.976 1 `0.981 7 0.951 6 0.966 4
600 0.987 2 0.972 5 0.979 8 0.983 2 0.952 0 0.967 3
1 200 0.985 6 0.970 1 0.977 8 0.980 8 0.950 5 0.965 4
1 800 0.984 7 0.966 6 0.975 6 0.980 4 0.948 6 0.964 2
2 400 0.981 4 0.964 4 0.972 8 0.978 8 0.941 5 0.959 8
Classification Effects after Extending Health Questions（TF-IDF）
保留词典
词语数（个）		 CBOW skip-gram
P R F P R F
300 0.954 3 0.925 0 0.939 4 0.931 4 0.921 4 0.926 4
600 0.957 2 0.932 1 0.944 5 0.954 7 0.928 8 0.941 6
1 200 0.961 5 0.940 1 0.950 7 0.958 8 0.936 7 0.947 6
1 800 0.965 9 0.941 6 0.953 6 0.963 3 0.938 3 0.950 6
2 400 0.962 1 0.938 9 0.950 4 0.960 1 0.937 2 0.948 5
Classification Effects after Extending Health Questions（LDA）
模型 P R F
SVM 0.945 7 0.939 1 0.942 4
未进行扩展的CNN 0.959 6 0.927 5 0.943 3
LDA提取关键词后扩展词向量特征 0.965 9 0.941 6 0.953 6
TF-IDF提取关键词后扩展词向量特征 0.987 2 0.972 5 0.979 8
Classification Effects of Different Models
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