Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis

Understand

Insightful findings in political science often require researchers to analyze documents of a certain subject or type, yet these documents are usually contained in large corpora that do not distinguish between pertinent and non-pertinent documents.

• In contrast, we can find corpora that label relevant documents but have limitations (e.g., from a single source or era), preventing their use for political science research.
• To bridge this gap, we present \textit{adaptive ensembling}, an unsupervised domain adaptation framework, equipped with a novel text classification model and time-aware training to ensure our methods work well with diachronic corpora.
• Experiments on an expert-annotated dataset show that our framework outperforms strong benchmarks.