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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (7): 52-60    DOI: 10.11925/infotech.2096-3467.2017.0484
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Multi-Label Classification of Chinese Books with LSTM Model
Sanhong Deng,Yuyangzi Fu(),Hao Wang
School of Information Management, Nanjing University, Nanjing 210023
Jiangsu Key Laboratory of Data Engineering and Knowledge Service (Nanjing University), Nanjing 210023, China
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[Objective] This paper proposes a new method to automatically cataloguing Chinese books based on LSTM model, aiming to solve the issues facing single or multi-label classification. [Methods] First, we introduced deep learning algorithms to construct a new classification system with character embedding technique. Then, we trained the LSTM model with strings consisting of titles and keywords. Finally, we constructed multiple binary classifiers, which were examined with bibliographic data from three universities. [Results] The proposed model performed well and had practical value. [Limitations] We only analyzed five categories of Chinese bibliographies, and the granularity of classification was coarse. [Conclusions] The proposed Chinese book classification system based on LSTM model could preprocess data and learn incrementally, which could be transferred to other fields.

Key wordsLSTM Model      Deep Learning      Character Embedding      Book Automatic Classification      Multi-label Classification     
Received: 27 May 2017      Published: 26 July 2017

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Sanhong Deng,Yuyangzi Fu,Hao Wang. Multi-Label Classification of Chinese Books with LSTM Model. Data Analysis and Knowledge Discovery, 2017, 1(7): 52-60.

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