## 1 Introduction  
Each writer's heritage contains a great amount of various types of works, from the writer's works, letters, and notes to the scientific papers, critics, memoirs, encyclopedia articles, etc. Conducting any kind of research based on heritage materials without the use of technical means is a very difficult task. The difficulty lies in both the significant volume of materials and the lack of obvious connections between them that leads researchers to spend a great amount of time to find appropriate sources and material through all of the writer's heritage. For example, dozens of scientific works, notes, and critiques can be written about any of the works, both during the writer's lifetime and after his death. A certain person can be a friend of the writer and a prototype of one of the heroes of the work or the hero himself. These types of relationships can be formed using natural language processing (NLP) approaches and stored in a database. Such a structure can be considered as a corpus of linked texts and used in digital humanities research. This corpus could provide a deeper understanding of the various literary works, allowing for the robust visualization of their relationships and enabling the search for more comprehensive and deep information within the texts. All these materials are connected by their mention of the writer's literary works, including some persons who interacted with the author, places where the author was, dates, and organizations connected with the author and the literary work (Figure 1). The research presented in this work is aimed to create a linked corpus over the literary heritage of the great Russian poet [PERSON]. Such a corpus would contain links between heritage parts based on the common entities between them. To extract these entities, we propose to use one of the Natural Language Processing approaches related to the search for named entities--Named Entity Recognition (NER)--and build a database which will store entities itself, including links from entities to texts in the corpus that, in general, will provide links between texts. Since the main language of the A.S. [PERSON] heritage is Russian, we additionally address some language-specific nuances, but the whole approach can be adapted to any language and any writer. When considering linguistic variety, given that languages such as the Russian language have particular nuances, creating and implementing customized NER systems becomes even more important. This paper also focuses on creating and deploying a unique NER system designed for the processing of the writer's literary heritage. Named Entity Recognition plays a crucial role in NLP and text analysis, where it is used to extract valuable information like people's names, locations, dates, organizations, and so on from the raw text [1]. The creation of the linking corpus for the writer's heritage could be based only on the extraction of common entities from the text and building relationships based on these entities, no matter which method is used. Its importance goes far beyond information extraction since NER is essential to many applications such as machine translation, text summarizing, information retrieval, and question-answering systems. Nowadays, with the huge amount of generated data, NER has become a crucial tool that will help academics, researchers, and organizations trying to analyze and extract the wealth of knowledge hidden in the textual corpus [2]. Research efforts in Named Entity Recognition focus mostly on the English language. Despite the growing interest in multi-language information extraction, few Russian-language studies report results. Although NER models for the English language have achieved impressive accuracy and usefulness levels, this is not necessarily the case for languages with less rich resources and morphological complexity like the Russian language.