Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling
Price statistics increasingly draw on scanner, web-scraped and receipt data, whose product descriptions are short, noisy and carry no standard product code, so each item must be coded to a consumption classification such as COICOP. National statistical offices already report that lightweight text classifiers are adequate for this task, but the published evidence is thin on dispersion, paired comparison, train-test overlap and measured computational cost. This paper supplies that evaluation. On an openly released synthetic benchmark of six COICOP-like categories, seven models are trained under one matched protocol and compared on accuracy and cost. A character n-gram logistic regression is...
| Authors | Vladimir Beskorovainyi |
|---|---|
| Primary category | cs.CL |
| Published | 1 June 2026 |
| Last updated at source | 3 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.