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Introduces GHI, a Graphormer-over-conditioned-hypergraph-incidence framework for aspect-based sentiment analysis that represents linguistic evidence as token–hyperedge incidence relations, achieving state-of-the-art results on six benchmarks with only 247M parameters.
This paper proposes a task-routed mixture-of-experts model with cognitive appraisal theory for implicit sentiment analysis, introducing auxiliary tasks to improve reasoning about sentiment from context and outperforming existing approaches.
This paper presents the construction of a Korean evaluation-annotated corpus (EVAD) for fine-grained aspect-based sentiment analysis in e-commerce reviews using Semi-Automatic Symbolic Propagation. It evaluates KoBERT and KcBERT models on the dataset, achieving high F1 scores in aspect-value pair recognition.