Year: 2022
Type: conference-paper
Abstract: 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.
Authors: Tanise Ceron, Nico Blokker, Sebastian Padó
Institutions: University of Stuttgart, University of Bremen
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W4385573096