arXiv Machine Learning June 2, 2026 By Vladimir Beskorovainyi

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

Read the original on arXiv Machine Learning →

arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.

By Sherri Weitl-Harms, John Hastings

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Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking

arXiv:2606. 04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.

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DaDaDa: A Dataset for Data Pricing in Data Marketplaces

arXiv:2607. 08785v1 Announce Type: cross Abstract: High-quality data drives machine learning advances across industries.

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Jul 14

Constraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification

arXiv:2607. 10588v1 Announce Type: new Abstract: Tasks such as customs tariff classification, export control categorization, and standards-based equipment coding require assigning an input instance to a fine-grained class under an explicit regulatory hierarchy.

By Siyu Wang, Wei Tan, Lulu Chen

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A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification

arXiv:2607. 18358v1 Announce Type: cross Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise.

By Bogdan Raduta, Horia Velicu, Alexandru Preda, Serban Chiricescu

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Discrete Diffusion Language Models Are Training-Free Multi-Label Classifiers

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