Papers by Benjamin C Warner
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. |
Utilizing Semantic Textual Similarity for Clinical Survey Data Feature Selection (2025.findings-acl)
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| Challenge: | Survey data often contains many features that lead to high-dimensional inputs for machine learning models. |
| Approach: | They propose to use semantic textual similarity scores to score features in surveys . they compare STS scores to a statistically-based scoring approach . |
| Outcome: | The proposed method can be used in clinical surveys to score features . it can be applied to a clinical dataset and to NIH All of Us dataset . |