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Attribute Value Extraction
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Attribute Value Extraction is the task of extracting values for a given set of attributes of interest from free text input. Attribute value extraction is for example applied in the context of e-commerce where product attribute values are extracted from product offers.
The related task Attribute Mining assume that the target attribute set is unknown, while attribute value extraction assumes that the attribute set is given. Multimodal Attribute Extraction aims at extracting attribute values from multi-modal input such as text plus images.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Automated Self-Refinement and Self-Correction for LLM-based Product Attribute Value Extraction
An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification
EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
PAE: LLM-based Product Attribute Extraction for E-Commerce Fashion Trends
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM
Using LLMs for the Extraction and Normalization of Product Attribute Values
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction
Multi-Label Zero-Shot Product Attribute-Value Extraction
Enhanced E-Commerce Attribute Extraction: Innovating with Decorative Relation Correction and LLAMA 2.0-Based Annotation
JPAVE: A Generation and Classification-based Model for Joint Product Attribute Prediction and Value Extraction
ExtractGPT: Exploring the Potential of Large Language Models for Product Attribute Value Extraction
AE-smnsMLC: Multi-Label Classification with Semantic Matching and Negative Label Sampling for Product Attribute Value Extraction
Knowledge-Enhanced Multi-Label Few-Shot Product Attribute-Value Extraction
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
CAVE: Correcting Attribute Values in E-commerce Profiles
Exploring Generative Models for Joint Attribute Value Extraction from Product Titles
Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction
OA-Mine: Open-World Attribute Mining for E-Commerce Products with Weak Supervision
MAVE: A Product Dataset for Multi-source Attribute Value Extraction
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
Disentangled Self-Supervision in Sequential Recommenders
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.