Towards Automatic Modelling of Thematic Domains of a National Literature: Technical Issues in the Case of Russian

A significant part of modern technologies associated with the development of artificial intelligence systems and digital analytics of diverse data relies on methods of computer text processing (NLP, speech technologies). However, NLP methods are applied primarily to specialized texts, such as scientific literature, technical documentation, news, etc., or social media discourse. Fiction texts are usually left out of the focus of NLP practitioners as the fictional world seems to be of less significance or less “information value” from a practical point of view. Moreover, due to the poetic and metaphorical nature of literary texts, the use of some NLP methods (e.g., topic modelling) for fiction analysis turned out to be more complicated. At the same time, the influence of literature both on the consciousness of individuals and on the formation of social values can hardly be overestimated. Besides, making computers “understand” fiction in a similar way as humans do would be a real challenge for artificial intelligence. The article puts forward the idea of modelling thematic areas of literature on a national scale, which should reveal the main thematic domains of national literature as a whole. It will allow a better understanding of the cultural traits of the national consciousness in a given historical period and contribute to either literary studies or practical tasks. Methodological approaches to determining and modelling themes of literary works are considered, technical difficulties arising in the process are described, and the ways to solve them are suggested. The proposed methodology has been implemented in the design of the corpus of Russian short stories of 1900-1930s and can be applied in the development of artificial intelligence systems that process large volumes of literary texts in any language.