Domain-Invariant Attribute Extraction via Domain-Adversarial Learning
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Solution Overview
Problem
Conventional attribute extraction systems are inefficient and labor-intensive due to the need for domain-specific rules and human intervention, especially when dealing with unstructured and semi-structured content across different web domains.
Innovation Solution
A domain-invariant attribute extraction model is trained using domain-adversarial learning to learn the semantics of attributes independently of the web domain, employing a framework with a domain classifier and gradient reversal mechanism to focus on attribute semantics while ignoring domain-specific information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific rules are used to train the attribute extraction model, then the extraction accuracy for structured content from known domains is improved, but the system complexity and human labor requirements increase significantly
Solution Approach 1:
The patent applies universality by training a single attribute extraction model on multi-domain data without requiring domain-specific rules. The model learns to extract attributes from multiple domains (e.g., Amazon, Etsy, LinkedIn) using a unified approach, eliminating the need for separate domain-specific rule sets and reducing overall system complexity while maintaining extraction accuracy.
Solution Approach 2:
The patent changes the fundamental parameters of the extraction system by transitioning from rule-based extraction with domain-specific parameters to a machine learning-based approach. The model uses neural network parameters trained on diverse domain data, allowing it to adapt to different content structures without manual rule configuration, thus reducing human labor and system complexity.
2Reliability
If domain-specific rules are created and maintained, then extraction performance for known domains is improved, but the time required for development and updates increases
Solution Approach 1:
The patent implements self-service by enabling the attribute extraction model to automatically adapt to new domains and content structures through machine learning. Instead of requiring manual creation and maintenance of domain-specific rules, the model learns from training data and can handle previously unseen domains autonomously, significantly reducing development time and ongoing maintenance requirements.
Solution Approach 2:
The patent applies preliminary action by pre-training the attribute extraction model on diverse multi-domain data before deployment. This pre-training equips the model with general attribute extraction capabilities that can be applied to new domains without requiring immediate manual rule creation, allowing for faster adaptation and reducing time-to-market for new domain integrations.
3Productivity
If conventional rule-based extraction is used, then extraction from structured content is effective, but it cannot handle unstructured and semi-structured content
Solution Approach 1:
The patent changes the fundamental approach from rigid rule-based extraction to flexible machine learning-based extraction. The neural network model can process various content structures (structured, semi-structured, and unstructured) by learning patterns from training data, enabling it to handle diverse content formats that would be impossible to cover with fixed rules while maintaining high extraction efficiency.
Solution Approach 2:
The patent achieves universality by creating a single attribute extraction model that can handle multiple content structures across different domains. The model is trained on diverse data including structured, semi-structured, and unstructured content, enabling it to universally extract attributes from any domain without requiring domain-specific rule sets or manual configuration.
Data Source
AI summary
The present teaching relates to attribute extraction from textual content. A domain-invariant attribute extraction model is trained for extracting predetermined attributes from textual content from multiple domains based on training data having a plurality of training samples, each with textual content, some of the predetermined attributes in the textual content, and a label indicating one of the multiple domains that produces the textual content. The domain-invariant attribute extraction model learns via training the semantics of the predetermined attributes across the multiple domains so that when a new textual content from any of the multiple domains, some predetermined attributes are extracted according to semantics thereof via the domain-invariant attribute extraction model.