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Authorship Attribution with Neural Networks and Multiple Features: Notebook for PAN at CLEF 2018

However neural networks are receiving more and more attention from different fields of computer-aided research, application of this approach to stylometry and authorship attribution is still relatively infrequent in comparison to other domains of natural language processing. In this paper we present our attempt to analyse frequencies of different types of linguistic data (Part-of-speech, most frequent words, n-grams and skip-grams) with the means of simple neural networks.