Papers by Charles Alba

2 papers
Towards Robust Sentiment Analysis of Temporally-Sensitive Policy-Related Online Text (2025.acl-srw)

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Challenge: Existing methods fail to adequately capture the temporal volatility inherent in policy-related sentiments, arguing that continuous time-series clustering and model merging achieve superior performance.
Approach: They propose to use continuous time-series clustering to select data points for annotation based on temporal trends and then apply model merging techniques.
Outcome: The proposed methods outperform existing methods by an average F1-score of 2.71% on temporally representative data.
CURA: Clinical Uncertainty Risk Alignment for Language Model–Based Risk Prediction (2026.acl-long)

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Challenge: Clinical language models (LMs) are increasingly applied to support clinical risk prediction from free-text notes, yet their uncertainty estimates are poorly calibrated and clinically unreliable.
Approach: They propose a framework that aligns clinical LM-based risk estimates and uncertainty with individual error likelihoods and cohort-level ambiguities.
Outcome: The proposed framework improves accuracy on clinical risk prediction tasks without compromising discrimination.

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