# Authorship Detection on Classical Chinese Text Using Deep Learning
## 3 Experiments
### SVM-Based Authorship Detection on DRC  
Table 1 lists 44 function words categorized into four different types: 13 literary-Chinese function words, 6 end-function words, 14 vernacular function words, and 10 transitional function words.  
The frequency of these 44 function words is calculated on a per-mileage scale as linguistic features. Training data, which include chapters 20 to 29 and 110 to 119, are input into the SVM model. Assuming that chapters 20 to 29 are written by author A and chapters 110 to 119 are written by author B, test data, which includes the remaining chapters, are input into the model and classified into two parts. The classification results are depicted in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Literary-Chinese** & **End-Function** & **Vernacular** & **Transitional** \\
**Function Words** & **Words** & **Function Words** & **Function Words** \\ \hline [PERSON] (\(\hat{\chi}\)), [PERSON] (\(\hat{\pi}\)), & & [PERSON] (\(\bar{\gamma}\)), [PERSON] (\(\hat{\#}\)i), & [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\bar{\gamma}\)), & & [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\bar{\
eg}\)), [PERSON] & [PERSON] (\(\bar{\pi}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\bar{\gamma}\)i), [PERSON] (\(\bar{\gamma}\)i), [PERSON] (\(\bar{\pi}\)i), [PERSON] (\(\bar{\mu}\)i), [PERSON] (\(\bar{\gamma}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] & [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\bar{\mu}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), & [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\hat{\#}\)i), [PERSON] (\(\bar{\mu}\)i), [PERSON] (\(\hat{\#}\Based on these classification results, a total of 67 chapters were written by author A and 53 chapters by author B. The boundary of changing writing habits is depicted in Figure 4.  
There is a clear boundary of inconsistency in writing habits between the 80 th and the 81 st chapters. Except for the 85 th chapter, the writing patterns of chapters 81 through 120 are the same. The majority of the writing patterns of the first 80 chapters are consistent, with only 14 chapters exhibiting inconsistent patterns, accounting for 17. 5%.  
Figure 5 presents the confusion matrix for the SVM-based method. Based on this matrix, precision, recall, and f1-score values are calculated. TP is equal to 66. FN is equal to 14. FP is equal to 1. TN is equal to 39. Precision is equal to TP/(TP+FP). Recall is equal to TP/(TP+FN). The combination of SVM and TF-IDF produces a good result, with precision values of 98.5%, recall values of 82.5%, and f1 scores of 89.8%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Chapters of Author A** & **Chapters of Author B** \\ \hline
1, 2, 3, 4, & \\
5, 7, 8, 9, & \\
10, 12, 13, 14, & 6, 11, 31, 32, \\
15, 16, 17, 18, & 45, 49, 56, 61, \\
19, 20, 21, 22, & 63, 65, 67, 68, 69, \\
23, 24, 25, 26, & 78, 81, 82, 83, \\
27, 28, 29, 30, & 84, 86, 87, 88, \\
33, 34, 35, 36, & 89, 90, 91, 92, \\
37, 38, 39, 40, & 93, 94, 95, 96, \\
41, 42, 43, 44, & 97, 98, 99, 100, \\
46, 47, 48, 50, & 101, 102, 103, 104, \\
51, 52, 53, 54, & 105, 106, 107, 108, \\
55, 57, 58, 59, & 109, 110, 111, 112, \\
60, 62, 64, & 113, 114, 115, 116, \\
66, 70, 71, 72, & 117, 118, 119, 120 \\
73, 74, 75, 76, & \\
77, 79, 80, 85 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of SVM-based classification.  
Figure 4: Change of authors’ writing habits.  
According to our classification results, the first 80 and last 40 chapters of DRC can be considered as literary works of two different authors.