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Open access Jul 2026

Label-guided data augmentation for cross-domain aspect-based sentiment analysis via enhanced affinity fusion

This work introduces an Enhanced Affinity Fusion module that explicitly strengthens aspect–opinion relational modeling by selectively integrating complementary attention mechanisms, and proposes Label-Guided Data Amplification (LGDA), which enhances supervision diversity and domain robustness through label-driven text expansion, hard sample mining, and domain-adaptive sampling.

Ningning Mao, Xuanliang Zhu, J. Wei et al. · 0 citations

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