# Post-Authorship Attribution Using Regularized Deep Neural Network
## 2 Related Works
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Features** & **Representation** &
\begin{tabular}{c} **Classification** \\ **Method** \\ \end{tabular} & **Visualization** & **Data** \\ \hline
[46] & Lexical &
\begin{tabular}{c} Character-level \\ n-grams \\ \end{tabular} & SVM & No & CCAT10 \\ \hline
[49] & Lexical and syntactic &
\begin{tabular}{c} TF-IDF over word, \\ character \\ \end{tabular} & SVM & No & English blog posts \\ \hline
[50] & Lexical and syntactic & \
\begin{tabular}{c} Character-level \\ n-grams \\ \end{tabular} &
\begin{tabular}{c} Similarity-based \\ methods \\ \end{tabular} & No & Blog posts \\ \hline
[51] & Lexical and syntactic &
\begin{tabular}{c} Character-level \\ n-grams, affix punctuation \\ 3-g \\ \end{tabular} & SVM & No & CCAT10, and CCAT50 \\ \hline
[52] & Lexical and syntactic &
\begin{tabular}{c} Frequent 3-g \\ \end{tabular} & SVM & No & CCAT10, and CCAT50 \\ \hline
[53] & Lexical and syntactic & \
\begin{tabular}{c} TF-IDF over \\ n-grams \\ \end{tabular} &
\begin{tabular}{c} Soft voting \\ ensemble \\ \end{tabular} & No & PAN 2019 \\ \hline
[55] & Lexical and syntactic &
\begin{tabular}{c} Punctuation marks, \\ name entity, and \\ character n-grams \\ \end{tabular} & SVM & No & CCAT10 \\ \hline
[61, 62, 63] & Lexical & \begin{tabular}{c} LDA, AT, \\ syntactic, and structural \\ \end{tabular} & \
\begin{tabular}{c} Probabilistic \\ Model \\ \end{tabular} & No &
\begin{tabular}{c} Judgment, email, \\ and IMDB \\ \end{tabular} \\ \hline
[60] & Lexical, syntactic, and structural & \
\begin{tabular}{c} Character, word, \\ and FastText \\ \end{tabular} & SoftMax & No &
\begin{tabular}{c} Judgment, IMDB62, \\ and CCAT50 \\ \end{tabular} \\ \hline
[68] & Lexical & \begin{tabular}{c} Weighting \\ n-gram \\ \end{tabular} & \
\begin{tabular}{c} CNG, SCAP, \\ and RLP \\ \end{tabular} & No &
\begin{tabular}{c} IRC, and \\ Twitter \\ \end{tabular} \\ \hline
[71] & Lexical and syntactic & \
\begin{tabular}{c} Character, \\ Word n-gram, and \\ K-signature \\ \end{tabular} &
\begin{tabular}{c} Linear \\ SVM \\ \end{tabular} & No & Twitter \\ \hline
[72] & Lexical & ChatSafe & NB & No & SMS \\ \hline
[73] & Lexical and syntactic & \
\begin{tabular}{c} POS tag \\ combine n-grams \\ \end{tabular} &
\begin{tabular}{c} Cosine \\ Similarity \\ \end{tabular} & No & Twitter \\ \hline
[74] & Lexical, syntactic, and structural & NLTP & SVM & No & Twitter \\ \hline
[75] & Lexical and syntactic & \begin{tabular}{c} Character and \\ word-level n-grams \\ \end{tabular} & \
\begin{tabular}{c} Word embedding, \\ CNN, and SoftMax \\ \end{tabular} & No &
\begin{tabular}{c} Email, Blogs, Redditt, \\ and Twitter \\ \end{tabular} \\ \hline
[77] & Lexical and syntactic & \
\begin{tabular}{c} Syntax Parse \\ Tree \\ \end{tabular} & CNN, SoftMax & No &
\begin{tabular}{c} CCAT10, CCAT50, \\ IMDB, and Blogs \\ \end{tabular} \\ \hline
[83] & Lexical and syntactic &
\begin{tabular}{c} POS tag and \\ word embedding \\ \end{tabular} & CNN, SoftMax & No & ARC \\ \hline
[85] & Lexical, syntactic, and structural &
\begin{tabular}{c} Character, word, n-gram, \\ and POS tags \\ \end{tabular} & CNN, SoftMax & No & Twitter and Weibo \\ \hline
[87] & Lexical, syntactic, and structural & BERT, RoBERT, and DistilBERT & SoftMax & No &
\begin{tabular}{c} CCAT10, IMDB, \\ and Reddit \\ \end{tabular} \\ \hline
**RDNN** & \begin{tabular}{c} **Lexical,** \\ **syntactic,** \\ **and structural** \\ \end{tabular} & \begin{tabular}{c} **Character-level** \\ **CNN and** \\ **DHN-BLSTM** \\ \end{tabular} & \begin{tabular}{c} **Dense and** \\ **SoftMax** \\ \end{tabular} & \
\begin{tabular}{c} **Yes** \\ **SoftMax** \\ \end{tabular} &
\begin{tabular}{c} **CCAT50, IMDB62, \\ **Blogs50, and Twitter50** \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._current study further addresses the knowledge gap by adopting a method that utilized the extracted features to visualize the results of the authorship attribution.