Attribute Value Extraction
12 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Attribute Value Extraction
Caselaw4
Most implemented papers
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
hackerxiaobai/OpenTag_2019
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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
hackerxiaobai/OpenTag_2019
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ACL 2019
Supplementing product information by extracting attribute values from title is a crucial task in e-Commerce domain.
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
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.
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
yeliu0930/Knowledge-guided-Open-Attribute-Value-Extraction-with-Reinforcement-Learning
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EMNLP 2020
Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy.
MAVE: A Product Dataset for Multi-source Attribute Value Extraction
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information.
CAVE: Correcting Attribute Values in E-commerce Profiles
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).
Product Attribute Value Extraction using Large Language Models
We propose different prompt templates for instructing LLMs about the target schema of the extraction, covering both zero-shot and few-shot scenarios.
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.
Multi-Label Zero-Shot Product Attribute-Value Extraction
We propose HyperPAVE, a multi-label zero-shot attribute value extraction model that leverages inductive inference in heterogeneous hypergraphs.
