Automatic Classification of Mechanical Engineering Terms: Experiments, Analysis, And Visualization Based on Artificial Intelligence

This article investigates the automatic classification of mechanical engineering terminology using artificial intelligence technologies. During the study, terminology units collected from a specialized corpus were analyzed through Natural Language Processing (NLP) and machine learning algorithms. The experiments employed TF-IDF, Word2Vec, Logistic Regression, Random Forest, and Support Vector Machine (SVM) models. The findings demonstrated the effectiveness of semantic and statistical approaches in the automatic classification of technical terminology. Furthermore, clustering and visualization techniques were used to identify semantic relationships among terms and represent them graphically. The results of the study provide an important scientific foundation for developing intelligent terminological databases for the Uzbek language.