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Found 4 papers, 4 papers with code
Using LLMs for the Extraction and Normalization of Product Attribute Values
In order to enable features such as faceted product search or to generate product comparison tables, it is necessary to extract structured attribute-value pairs from the unstructured product titles and descriptions and to normalize the extracted values to a single, unified scale for each attribute.
Ranked #1 on
Attribute Value Extraction
on WDC-PAVE
ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction
Vendors often times provide unstructured product descriptions consisting only of an offer title and a textual description.
Ranked #1 on
Attribute Value Extraction
on AE-110k
(F1-score metric)
Product Information Extraction using ChatGPT
Hence, extracting attribute/value pairs from textual product descriptions is an essential enabler for e-commerce applications.
SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines
To reduce these runtimes, entity resolution pipelines are constructed of two parts: a blocker that applies a computationally cheap method to select candidate record pairs, and a matcher that afterwards identifies matching pairs from this set using more expensive methods.
Ranked #1 on
Blocking
on Amazon-Google
