МЕТОДЫ АВТОМАТИЧЕСКОГО ИЗВЛЕЧЕНИЯ КЛЮЧЕВЫХ СЛОВ В УЗКОСПЕЦИАЛИЗИРОВАННЫХ ТЕКСТАХ ДЛЯ ПОСТРОЕНИЯ СЛОВАРЕЙ
Authors: Пуртов Д.Н. , Кубашева Е.С. 
Published: February 2025

В данной статье рассмотрены методы извлечения ключевых слов из узкоспециализированного текста, рассмотрены их достоинства и недостатки. На основе проведённых исследований был предложен метод извлечения ключевых слов из узкоспециализированного текста для построения словарей, в основе которого лежит нейросетевая модель статистического типа, отличающаяся от известных способом её формирования в процессе обучения через укрепление нейронных связей в ходе обучения. Таким образом, механизм работы модели реализует статистический подход с использованием компонентов машинного обучения. Особенностью предложенного метода является использование дополнительных связей между нейронами, которые создаются в ходе обучения.

Introduction. Active growth of available text information has significantly accelerated the development of a scientific field that has existed for many decades and is known as automatic text processing (Natural Language Processing). Currently, the problem of developing methods for extracting keywords from narrowly focused texts is topical for many areas of industry, economic and social activities. The development of artificial intelligence methods (namely, the use of neural networks), machine learning has allowed to increase the efficiency of solutions in this area. The aim of the research is to develop a method that allows to extract keywords from texts with a limited number of linguistic corpora with greater accuracy and fewer errors. For the analysis, statistical methods and methods based on machine learning were considered. Findings. The analysis showed that statistical methods do not require high-performance actions, such as, for example, building connections of words with each other or morphological analysis of each word. Their main drawback is low efficiency, which is associated with the presence of synonyms, homonyms, etc. in texts. Training a model with the use of machine learning employs data that require additional resources for their gathering. But at the same time, the quality of extracting keywords from the text increases because the neural network model takes into account all kinds of connections that statistical methods do not take. It was shown that the methods based on machine learning can be considered as the evolution of static methods, with the main drawback of the need to train the model. The paper proposes the use of a neural network model and a method based on it for narrowly focused texts. There is implemented statistical analysis of the text and its drawbacks are mitigated by the advantages of methods based on machine learning. The paper analyzes the known methods of extracting keywords from narrowly focused text, and considers their benefits and drawbacks. In addiiton to this, in narrowly focused texts, the low number of text corpora for creating a model based on machine learning can lead to a large number of errors. To improve the efficiency of extracting keywords from the text, there is proposed a method based on a neural network model. It differs from the known methods by the model formation technique in the learning process through the reuse of training data.