Top-K Hierarchical Classification for Precision in Automotive Technical Data Analysis

This paper introduces an advanced Top-K hierarchical classification model specifically designed for technical Ukrainian texts within the automotive industry. Addressing the unique challenges of complex terminology, mixed-language usage, and frequent data inconsistencies, the study enhances Natural Language Processing (NLP) techniques through a robust classification framework. The Top-K approach allows the model to generate multiple high-confidence class predictions, improving accuracy and decision-making processes, particularly in scenarios involving ambiguous or unlabeled data. Coupled with advanced data preprocessing methods, including language identification, tokenization, and lemmatization, the model achieves significant improvements in classification accuracy, ranging from 85% to 98% across various datasets. This Top-K hierarchical model also supports real-time applications, making it highly effective for use in automotive diagnostics, repair scheduling, and other industry-specific tasks. The research underscores the value of tailored NLP solutions for specialized domains and highlights the potential for broader application of the Top-K classification approach in technical fields. Future work will explore further enhancements and applications of this model to complex data extraction and classification tasks within the automotive industry and beyond.