# Automated Dataset-Creation and Evaluation Pipeline for NER in Russian Literary Heritage
## 1 Introduction  
One of the core tasks in Natural Language Processing (NLP) is Named Entity Recognition (NER), which is the process of recognizing and categorizing entities in text, including names of individuals, places, organizations, and other proper objects. NER systems are critical for many applications, including machine translation, question answering, keyword searching, and information retrieval [1, 2, 3]. Even while NER for languages like English has advanced significantly, creating reliable NER systems for other languages from the same language branch like, for example, German or Dutch (West Germanic) [4, 5] and even for a language from a different language branch like Russian (Balto-Slavic branch) is still a difficult task [6]. In recent years, researchers have shown significant interest in NER, and many multilingual NER models now extract basic entities from multiple languages (including Russian) [7]. The other challenge is a creation of NER systems for other domains [4, 8, 9], especially NER systems for literature purposesor legal document analysis since they provide a vast amount of synonyms, polysemies, word forms, and other domain-specific techniques for expressing ideas [6; 10]. General multilingual models struggle when dealing with any specific domain because they are trained on general texts extracted from the Internet like Wikipedia and cannot recognize domain-specific entities even for known entity types [3; 4; 5; 8]. One of the major problems with these issues is the lack of domain-specific datasets as well as task-specific pre-trained models for languages [5]. This highlights the need to create a custom dataset focused on a specific domain, enabling the training and fine-tuning of NER models to achieve accurate results when extracting named entities from texts. This study examines the development of an automated approach for generating a custom dataset to support named entity-recognition tasks. The literature heritage of [PERSON] was chosen as a specific domain since it contains significant amount of specific examples of NER entities. The dataset is characterized by its focus on the following types of frequently encountered entities:  
1. Persons--The texts include numerous references to individuals, and the identification of these entities facilitates the reconstruction of [PERSON]'s social network during the creation of his literary works. 2. Locations--These entities encompass both real and fictional places. The recognition of real locations enables an analysis of the geographical context of [PERSON]'s creative activities and correspondence, while fictional locations provide insights into the settings described in his literary compositions. 3. Dates--Identifying dates in texts concerning [PERSON]'s works contributes to the development of a chronology of his creative process, which is essential for enriching his biography. 4. Organizations--Throughout his life, [PERSON] interacted with numerous organizations, including educational institutions, publishing houses, censorship committees, and others. Recognizing mentions of these organizations in texts broadens our understanding of his life and creative journey. 5. Works by [PERSON]--Texts about [PERSON]'s works often reference other literary compositions, both by [PERSON] himself and by other authors. This category is particularly challenging to identify due to the frequent use of multiple and varying titles for the same work. None of the mentioned entity types can be recognized by general models with enough precision and recall to process texts about works of [PERSON] or other writers (Section 5). That means a lot of manual work editing results and datasets for custom model training. Creating custom NER datasets is often hindered by significant barriers, particularly in terms of time and cost. Building high-quality NER requires accurate attention to every detail to identify the entities' spans in a text and correctly label them. This is without accounting for the additional time required for proofreading and pre-processing.