# Post-Authorship Attribution Using Regularized Deep Neural Network
## 2 Related Works
The syntactic characteristics are content-dependent, more robust, and effective against the topic variance. The authors in [60] used continuous representation through a neural network combined with a classification layer for authorship attribution. They reached an accuracy of 94.8% on the IMDB62 dataset [80]. The authors in [75] applied a CNN model for a large-scale task of authorship attribution based on the work reported in [81]. They evaluated their method on different text data such as emails, reviews, blogs, and tweets to obtain excellent accuracy results between 85.0% and 95.0% across the datasets used. Three language methods of lexical, syntactic, and character-level features were combined in [1] for authorship identification using the international conference on weblogs and social media (ICWSM) labeled Twitter datasets [82] to achieve the best accuracy of 96.3%. The authors in [76] used CNN based on a sequence of character n-grams for the authorship-attribution problem of short posts. They evaluated their method using the dataset presented in [71]. Besides showing a satisfactory result of 76.1% of accuracy for 50 authors, they noticed that nearly 30.0% of the users presented in the dataset behaved as automated bots, turning the task into a straightforward method. However, when the profiles were removed, the classification accuracy dropped to 68.3% for 35 authors. The researchers in [83] tackled the problem of authorship attribution using a single author from an anthology reference corpus [84] and POS tags with the application of a CNN method. They reached a better generalization by replacing uncommon words with their POS tags to achieve good results from a scientific publications corpus. The authors in [85] used different types of embedding structures based on character, word, \(n\)-gram, and POS tags in a CNN model to learn the representations of writing styles of authors of Twitter and Weibo posts. They adopted the dataset in [71] to obtain accurate results for 50 authors below the results reported in [76]. The authors in [77] introduced a syntax encoding using the CNN collaborated with lexical features for authorship attribution. They utilized a syntax parse tree of sentences to achieve accuracy results of 81.00%, 96.16%, and 56.73% on the CCAT50, IMDB62, and Blogs50 datasets, respectively. The authors in [78] introduced syntactic and lexical representations using the attention-based hierarchical neural network to encode the syntactic, and semantic structures of sentences in documents to respectively achieve the accuracy results of 82.35% and 73.83% on CCAT50 and Blogs50 datasets [86]. The authors in [87] used a method based on pretrained language models such as bidirectional encoder representations from Transformers (BERT) [88], pretraining a general-purpose language representation of DistilBERT [89], and a robustly optimized BERT pretraining (RoBERTa) [90] on a large collection of highly diverse authorship-attribution datasets to provide better generalization results. Previous works use this family of models combined with a dense layer and a SoftMax function to extract stylometric features of character-level bi-gram and trigram in an ensemble model [39]. They achieved an accuracy of 93.0% at 10 epochs that did not perform well on IMDB62 datasets [80]. Furthermore, the method reached a relative improvement of 5.3% accuracy on a Blog dataset with 50 authors compared to the previous techniques [77]. Table 1 provides a comparison of related studies based on common characteristics.