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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (3): 25-34    DOI: 10.11925/infotech.2096-3467.2019.1033
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Deep Learning Based Automatic Sentence Segmentation and Punctuation Model for Massive Classical Chinese Literature
Wang Qian1,Wang Dongbo1,2(),Li Bin3,Xu Chao3
1College of Information Management, Nanjing Agricultural University, Nanjing 210095, China
2Research Center for Correlation of Domain Knowledge, Nanjing Agricultural University, Nanjing 210095, China
3College of Literature, Nanjing Normal University, Nanjing 210097, China
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Abstract  

[Objective] This study establishes an annotation system with cascaded deep learning model, aiming to automatically conduct sentence segmentation and punctuation for ancient Chinese literature. [Methods] First, we created a massive corpus of Chinese books from “Siku Quanshu”. Then, we studied the automatic sentence segmentation and punctuation as sequence labeling issues, and determined the cascaded ideas. Third, we obtained the results of automatic sentence segmentation for the uninterrupted sentences based on the BERT-LSTM-CRF model. Fourth, we processed these results with the multi-feature LSTM-CRF model and received the final punctuation marks after iterative learning. [Results] We built an application platform with the trained model and the Django framework. The average F values of the proposed method for automatic sentence segmentation and punctuation were 86.41% and 90.84%, respectively. [Limitations] The punctuation system needs to be refined. [Conclusions] The proposed model and platform significantly improve the sentence segmentation and punctuation of ancient Chinese literature, which benefits digital humanity and social science projects in China.

Key wordsAutomatic Sentence Segmentation      Digital Humanities      BERT      Ancient Chinese     
Received: 11 September 2019      Published: 12 April 2021
ZTFLH:  G255  
Fund:National Natural Science Foundation of China(71673143);National Social Science Fund of China(15ZDB127)
Corresponding Authors: Wang Dongbo     E-mail: db.wang@njau.edu.cn

Cite this article:

Wang Qian,Wang Dongbo,Li Bin,Xu Chao. Deep Learning Based Automatic Sentence Segmentation and Punctuation Model for Massive Classical Chinese Literature. Data Analysis and Knowledge Discovery, 2021, 5(3): 25-34.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.1033     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I3/25

Contextual Word Embedding by BERT
LSTM Model
Flow Chart of the Experiment
类别 训练集 验证集 测试集 总计
经部 4 572 819 575 947 572 576 5 721 342
史部 31 446 274 3 920 904 3 930 548 39 297 726
子部 19 434 858 2 426 688 2 428 228 24 289 774
集部 26 795 226 3 343 104 3 344 001 33 482 331
Data Size of Each Category of Ancient Books
Schematic Diagram of BERT-LSTM-CRF
LSTM-CRF Model with Multiple Features
观测序列 5-tag
S
B
I
I
I
J
E
B
J
E
Example of Labeling System of BERT-LSTM-CRF
观测序列 特征 标签
B O
I O
J O
E D
B O
I O
J- O
E D
B O
I O
J O
E D
B O
I O
I O
I O
I O
J O
E J
Example of Labeling System of LSTM-CRF with Multiple Features
The Evaluation of Automatic Sentence Segmentation and Punctuation Model
The Effects of Pre-training Models on Sentence Segmentation
指标 S(书名号) W(问号) F(分号) G(感叹号) D(逗号) M(冒号) J(句号) 总计
P 92.98 83.39 63.40 70.81 90.73 97.14 91.55 91.05
R 91.45 87.22 37.90 38.76 94.80 95.63 87.88 91.08
F 92.21 85.26 47.44 50.10 92.72 96.38 89.42 91.07
The Results of Automatic Punctuation for Confucian Classics (%)
Home Page of Automatic Punctuating Platform for Classical Chinese
Page for Segmenting and Punctuating Sentences Automatically
Page for Segmenting and Punctuating Texts Automatically
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