AI 中文总结
本文比较了分类与回归方法在政治文本尺度化任务中的表现，探讨联合预测与中间方案的有效性。
AI 中文摘要
文本尺度化，即将政治行为者定位在意识形态尺度上的任务，是政治分析中的基本任务。为了减少手动分析的需求，已经提出了各种NLP方法用于此任务，包括基于分类和回归的方法，这些方法既取得了成功也存在局限性。本文的目标是整合该领域的最新技术。我们提出两个问题：(a) 通过联合预测而非单独预测尺度，能否提高尺度化方法的性能？(b) 在分类和回归之间是否存在中间地带？
英文摘要
Text scaling, the task of positioning political actors on an ideological scale, is a fundamental task in political analysis. To ease the need for manual analysis, various NLP methods have been proposed for this task, including classification- and regression-based approaches, showing successes as well as limitations. The goal of our paper is to consolidate the state of the art in this area. We ask two questions: (a) Can the performance of scaling methods be improved by predicting scales not individually but jointly? (b) Is there a middle ground between classification and regression?
Comments6th Workshop on Computational Linguistics for the Political and Social Sciences, Hamburg, September 2026
