Summary of A Framework For Leveraging Partially-labeled Data For Product Attribute-value Identification, by D. Subhalingam et al.
A Framework for Leveraging Partially-Labeled Data for Product Attribute-Value Identification
by D. Subhalingam, Keshav Kolluru, Mausam, Saurabh Singal
First submitted to arxiv on: 17 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces GenToC, a neural model designed for training with partially-labeled data to accurately extract attribute-value pairs from product titles and user search queries in e-commerce domains. This is crucial for enhancing search and recommendation systems. The model employs a marker-augmented generative model to identify potential attributes, followed by a token classification model that determines associated values. GenToC outperforms existing state-of-the-art models, achieving up to 56.3% increase in accurate extractions. Additionally, the paper demonstrates GenToC’s ability to regenerate training datasets to expand attribute-value annotations, improving data quality for other NER models. The results demonstrate GenToC’s unique ability to learn from limited partially-labeled data and improve the training of more efficient models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GenToC is a new way to help computers understand product information. Right now, searching for products online can be tricky because computers don’t always get what we mean. To make it better, GenToC uses special computer programs to find important details like “Brand: Apple” from product names and search queries. This helps with recommendations and searches on websites. The problem is that training these programs requires lots of correct information, but that’s hard to come by. GenToC can learn from incomplete data and make the other programs better too. It even works well in real-life situations like IndiaMART, a big online marketplace. |
Keywords
» Artificial intelligence » Classification » Generative model » Ner » Token
