Domain Adaptation for Text Classification via Feature Segmentation
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Solution Overview
Problem
Traditional domain adaptation methods for text classification face challenges such as negative transfer of features and limited performance due to the lack of explicit exclusion of source-specific representations and missed discriminative features in the target domain, leading to suboptimal results in transferring knowledge from a source domain to a target domain.
Innovation Solution
A domain adaptation method and system that learns a common representation between the source and target domains using a domain regression processor, labels unlabeled instances in the target domain, and determines a target-specific representation to train a classifier for automatic text classification, thereby mitigating negative transfer and enhancing classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional domain adaptation methods learn a common representation between source and target domains, then knowledge transfer from source to target domain is enabled, but negative transfer of domain-specific features occurs and classification accuracy deteriorates
Solution Approach 1:
The patent segments the representation learning process into two distinct components: a common representation module that learns domain-invariant features shared between source and target domains, and a domain-specific representation module that captures features unique to each domain. This segmentation prevents negative transfer by allowing the system to selectively apply common features while preserving domain-specific discriminative information through separate processing pathways.
Solution Approach 2:
The patent extracts and removes domain-specific features from the common representation learning process. By explicitly identifying and separating domain-specific features through domain adaptation techniques, the system prevents these features from causing negative transfer while still leveraging them for domain-specific tasks through the extracted domain-specific representation component.
2Device complexity
If traditional methods do not explicitly exclude source-specific representations, then the system complexity remains low, but discriminative features in the target domain are missed and performance is limited
Solution Approach 1:
The patent introduces a segmented architecture with distinct modules for common representation learning and domain-specific representation learning. This segmentation, while increasing system complexity, enables the explicit exclusion of source-specific representations that would otherwise cause negative transfer, thereby improving target domain classification performance through more precise feature selection.
3Ease of manufacture
If brute force transfer of source domain features is applied, then the implementation process is simple, but negative transfer occurs and target domain learning is adversely affected
Solution Approach 1:
The patent extracts only the beneficial common features from the source domain while explicitly removing harmful domain-specific features before transferring to the target domain. This extraction process replaces simple brute force transfer with a selective feature transfer mechanism that maintains implementation feasibility while significantly improving target domain learning quality by preventing negative transfer.
Data Source
AI summary
The disclosed embodiments illustrate a domain adaptation method for learning transferable feature representations from a source domain for a target domain. The method includes receiving input data comprising a plurality of labeled instances of the source domain and a plurality of unlabeled instances of the target domain. The method includes learning common representation shared between the source domain and the target domain, based on the plurality of labeled instances of the source domain. The method includes labeling one or more unlabeled instances in the plurality of unlabeled instances of the target domain, based on the common representation. The method includes determining a target specific representation corresponding to the target domain. The method includes training a target specific classifier based on the target specific representation and the common representation to perform text classification on remaining one or more unlabeled instances of the plurality of unlabeled instances of the target domain.