# Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

arXiv: https://arxiv.org/abs/2302.12139 · HTML: https://mlsanity.com/paper/2302.12139/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [MAVE: A Product Dataset for Multi-source Attribute Value Extraction](https://mlsanity.com/paper/2112.08663/) (2021)
- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://mlsanity.com/paper/2111.02705/) (2021)
- [Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products](https://mlsanity.com/paper/2105.08956/) (2021)
- [GreenDB: Toward a Product-by-Product Sustainability Database](https://mlsanity.com/paper/2205.02908/) (2022)
- [GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods](https://mlsanity.com/paper/2207.10733/) (2022)

## Abstract (arXiv)

Extracting structured information from unstructured data is one of the key challenges in modern information retrieval applications, including e-commerce. Here, we demonstrate how recent advances in machine learning, combined with a recently published multilingual data set with standardized fine-grained product category information, enable robust product attribute extraction in challenging transfer learning settings. Our models can reliably predict product attributes across online shops, languages, or both. Furthermore, we show that our models can be used to match product taxonomies between online retailers.