Mapping political party systems to metric policy spaces is one of the major methodological problems in political science. At present, in most political science project this task is performed by domain experts relying on purely qualitative assessments, with all the attendant problems of subjectivity and labor intensiveness. We consider how advances in natural language processing, including large transformer-based language models, can be applied to solve that issue. We apply a number of texts similarity measures to party political programs, analyze how they correlate with each other, and -- in the absence of a satisfactory benchmark -- evaluate them against other measures, including those based on expert surveys, voting records, electoral patterns, and candidate networks. Finally, we consider the prospects of relying on those methods to correct, supplement, and eventually replace expert judgments.

翻译：将政党体系映射到度量政策空间是政治学中主要的方法论问题之一。目前，多数政治学项目依赖领域专家通过纯定性评估完成该任务，由此带来主观性强和劳动密集型等问题。我们探究自然语言处理领域的最新进展——包括基于Transformer的大型语言模型——如何应用于解决该问题。我们采用多种文本相似度计算方法对政党政治纲领进行分析，考察不同方法间的相关性，并在缺乏满意基准的情况下，将其与基于专家调查、投票记录、选举模式及候选人网络等方法的评估结果进行对比。最终，我们探讨了依赖这些方法修正、补充乃至最终取代专家判断的前景。