%0 Conference Proceedings %T Optimizing text representations to capture (dis)similarity between political parties %A Ceron, Tanise %A Blokker, Nico %A Padó, Sebastian %Y Fokkens, Antske %Y Srikumar, Vivek %S Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL) %D 2022 %8 December %I Association for Computational Linguistics %C Abu Dhabi, United Arab Emirates (Hybrid) %F ceron-etal-2022-optimizing %X 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. %R 10.18653/v1/2022.conll-1.22 %U https://aclanthology.org/2022.conll-1.22 %U https://doi.org/10.18653/v1/2022.conll-1.22 %P 325-338