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| Reducing Dimensions of Custom Declaration Texts with Word2Vec |
Gong Lijuan,Wang Hao( ),Zhang Zixuan,Zhu Liping |
School of Information Management, Nanjing University, Nanjing 210023, China
Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing 210023, China |
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Abstract [Objective] This study tries to reduce the dimension of custom declaration texts, aiming to improve the efficiency of custom platforms.[Methods] We collected the declaration texts from a Chinese custom in four months as the corpus. Then, we evaluated the quality of the word vectors from the microscopic perspectives of word similarity and relevance. We also combined the traditional 0-1 matrix, frequency reduction and information gain with the SVM algorithm. Finally, we compared the results of these methods with the performance of Word2Vec word vector.[Results] Word2Vec word vector is an ideal dimension reduction method for customs declaration texts, and the classification efficiency was the highest when the word vector dimension reached 500, and the accuracy rate was 93.01%.[Limitations] We only studied the five categories with larger data volume.[Conclusions] The proposed method ensures data accuracy and integrity, which significantly reduces feature dimensions.
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Received: 05 June 2019
Published: 26 April 2020
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Corresponding Authors:
Hao Wang
E-mail: ywhaowang@nju.edu.cn
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