شناسایی جنسیت خودکار نویسندۀ متن روسی با استفاده از یادگیری ماشین و الگوریتم های شبکه عصبی در مورد فریب جنسیتی

We present the analysis of approaches to solve an author gender identification task for Russian-language texts with gender deception, using different Data-Driven models based on conventional machine learning (Support Vector Classifier, Decision Tree, Gradient Boosting) and neuronet algorithms (convolutional layers, long short-term memory layers, etc.) The source of training and testing data are collections of texts from the Gender Imitation corpus, expanded by crowd-sourcing and supplemented with files of RusProfiling and RusPersonality corpora. The reached accuracy of this task milestone is presented and discussed.