Attribute Value Extraction
10 papers with code • 0 benchmarks • 1 datasets
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OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
We study this problem in the context of product catalogs that often have missing values for many attributes of interest.
Scaling up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain.
We annotate a multimodal product attribute value dataset that contains 87, 194 instances, and the experimental results on this dataset demonstrate that explicitly modeling the relationship between attributes and values facilitates our method to establish the correspondence between them, and selectively utilizing visual product information is necessary for the task.
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information.
To the best of our knowledge, CAVE is the first system that allows users to experiment with a number of powerful QA models and compare their performances on attribute values correction using real-word datasets.
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
In this paper, we reformulate this task as a multi-label classification task that can be applied for real-world scenario in which only annotation of attribute values is available to train models (i. e., annotation of positional information of attribute values is not available).
E-commerce applications such as faceted product search or product comparison are based on structured product descriptions like attribute/value pairs.
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction
Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text.