Even though fine-tuned neural language models have been pivotal in enabling
"deep" automatic text analysis, optimizing text representations for specific
applications remains a crucial bottleneck. In this study, we look at this
problem in the context of a task from computational social science, namely
modeling pairwise similarities between political parties. Our research question
is what level of structural information is necessary to create robust text
representation, contrasting a strongly informed approach (which uses both claim
span and claim category annotations) with approaches that forgo one or both
types of annotation with document structure-based heuristics. Evaluating our
models on the manifestos of German parties for the 2021 federal election. We
find that heuristics that maximize within-party over between-party similarity
along with a normalization step lead to reliable party similarity prediction,
without the need for manual annotation.

本研究旨在解决 fine-tuned 神经语言模型在特定应用中的文本表示优化问题，结合德国政党清单，通过使用基于文档结构启发式策略、最大化党内相似性及标准化等一系列措施，实现对政党相似性的可靠预测，无需人工注释。