title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð´Ð»Ñ ÑÐ¸ÑÑÐµÐ¼Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð±Ð°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ñ Ð·Ð°Ð´Ð°Ð½Ð¸Ñ Ð½Ð° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð¼ ÑÐ·ÑÐºÐµ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Research and development of methods and algorithms for the system of an automated database model generation based on the natural language task text
Creators: name: ÐÐ°Ð¿Ð¸Ð½, ÐÐ³Ð¾ÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐ°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° ÐµÑÑÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ·ÑÐºÐ°
Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐºÑÑÐ°
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
natural language processing
text analysis
classification

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description: This paper describes the problem of a high level of resource and effort needs that are required during the process of database modelling. That is said, while also there is a high demand for such tasks during the development of almost any IT system, as for business, individual or educational purposes. Therefore, a solution is proposed â an automated system of database model generation based on the natural language task text should be developed. So, this paper describes the overall problems that are faced with computers trying to understand natural language, while also methods and algorithms are proposed for the mentioned system prototype development. Also, different techniques of machine learning, such as transformers, Named Entity Recognition, Relation Extraction, classification, and morphological analysis, are described and used. During the development of the mentioned prototype a training dataset is composed and annotated alongside with custom neural multicomponent network pipeline being developed and trained. In addition, the following program components are being developed: Java API for user interaction and a Python microservice, which is suited for interaction between main API and neural model pipeline. At the end of the day, the developed prototype has been successfully tested and used to generate a database model based on the given task text in natural language. Also, the developed system has shown good results in comparison with other solutions that are based on Large Language Models, such as YandexGPT and Sber GigaChat, and even outperformed the first one.