Identification and Harmonization of Material Values and Product Names in a Group of Companies Using Nlp Methods
DOI:
https://doi.org/10.46991/BYSU.G.2026.17.1.083Keywords:
Material classification, data standardization, ERP systems, Master Data Management, Entity Resolution, Named Entity Recognition, NLP, precision, recall, F1-scoreAbstract
The article examines the problem of heterogeneous material-value and product names in a group of companies. The same physical material may be registered under different abbreviations, spellings, languages, internal codes, or incomplete descriptions in the accounting and enterprise systems of separate subsidiaries. This reduces the quality of consolidated reporting, complicates procurement analysis, inventory control, price comparison, and managerial decision-making at group level. The problem is formulated as an Entity Resolution and product-matching task and is addressed through Natural Language Processing and machine learning methods. A dataset of 17,258 material and product names was annotated manually and used to train a domain-specific Named Entity Recognition model. The proposed pipeline extracts structured components from free-text descriptions and creates a basis for unified material classification, centralized procurement, and analytical control in a group of companies. The article also adds a model-evaluation framework based on the confusion matrix, precision, recall, and F1-score.
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Copyright (c) 2026 Rafik Mashuryan
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
