Methods for Automatic Assignment of UDC Codes to Mathematical Articles: an Evaluation of Classical and Neural Approaches
Year: 2026
Type: article
Abstract: Universal Decimal Classification (UDC) is a hierarchical indexing system in which a publication may be assigned one or several codes. Manual UDC indexing is labor-intensive and often inconsistent. This paper addresses the automatic assignment of UDC codes to Russian-language mathematical research articles. The aim is to compare combinations of text representations and classification models on a unified corpus and to identify the most effective configurations. A corpus of 4194 articles was collected from Math-Net.Ru, including full texts, abstracts, metadata, and UDC codes. The preprocessing pipeline comprised PDF text extraction, removal of layout artifacts, and normalization of UDC labels. We compared TF-IDF, Word2Vec, SciRus-tiny, and SciRus-tiny3.5 representations combined with logistic regression, Complement Naive Bayes (CNB), and CatBoost. In both the single-label and multi-label settings, the best performance was achieved by TF-IDF + LogReg, while TF-IDF + CNB showed closely competitive results. The proposed approach can be used in automatic subject indexing systems for digital libraries and scientific archives, in UDC recommendation tools for authors and editors, and in metadata quality control workflows.
Institution: Kazan Federal University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7165164850