Computer aided functional style identification and correction in modern russian texts

Functional style (FS) identification is a classification task in linguistics that categorizes unrestricted texts into several categories of linguistic norms. FS is widely used to attain a satisfying outcome in style processing. As such, we train a deep learning attention neural network model on modern-Russian texts, divide them into four FS categories. The model obtained an accuracy of 0.72. In particular, 81.08% and 85.71% accuracy in classifying the artistic and academic FS. The proposed model is able to automate the FS identification process and aids both domain experts and non-domain experts to perform FS correction by highlighting style anomalies concerning a desired style for the text. In particular, we show a 34% and 31% average improvement in the duration of performing the style correction task. Moreover, domain experts and non-domain experts obtain 3% and 9% more accurate results, respectively.