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.

采用自然语言处理技术中的大型变形器模型，对政党政治纲领进行相似度测量，并分析它们与专家调查、选民投票记录及候选人网络等指标的相关性，考虑了这种方法替代专家判断的前景。