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The ability to automatically determine the age audience of a novel provides many opportunities for the development of information retrieval tools. Firstly, developers of book recommendation systems and electronic libraries may be interested in filtering texts by the age of the most likely readers. Further, parents may want to select literature for children. Finally, it will be useful for writers and publishers to determine which features influence whether the texts are suitable for children. In this article, we compare the empirical effectiveness of various types of linguistic features for the task of age-based classification of fiction texts. For this purpose, we collected a text corpus of book previews labeled with one of two categories -children's or adult. We evaluated the following types of features: readability indices, sentiment, lexical, grammatical and general features, and publishing attributes. The results obtained show that the features describing the text at the document level can significantly increase the quality of machine learning models. Keywords: Text classification • Fiction • Corpus • Age audience • Content rating • Text difficulty • RuBERT • Neural network • Natural language processing • Machine learning Supported by the grant of the President of the Russian Federation no. MK-637.2020.9.

This paper describes neural models developed for the Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages Shared Task 2021. Our team called neuro-utmn-thales participated in two tasks on binary and fine-grained classification of English tweets that contain hate, offensive, and profane content (English Subtasks A & B) and one task on identification of problematic content in Marathi (Marathi Subtask A). For English subtasks, we investigate the impact of additional corpora for hate speech detection to fine-tune transformer models. We also apply a one-vs-rest approach based on Twitter-RoBERTa to discrimination between hate, profane and offensive posts. Our models ranked third in English Subtask A with the F1-score of 81.99% and ranked second in English Subtask B with the F1-score of 65.77%. For the Marathi tasks, we propose a system based on the Language-Agnostic BERT Sentence Embedding (LaBSE). This model achieved the second result in Marathi Subtask A obtaining an F1 of 88.08%.

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