Home | Search | All Publications | Authors | About

BibTeX

@InProceedings{blokker22:_why_justif_claim_matter_under_party_posit,
  author =       {Nico Blokker and Tanise Ceron and Andre Blessing and Erenay Dayanik and Sebastian Haunss and Jonas Kuhn and Gabriella Lapesa and Sebastian Padó},
  title =        {Why Justifications of Claims Matter for Understanding Party Positions},
  keywords =     {myown workshop},
  booktitle = {Proceedings of the 2nd Workshop on Computational Linguistics for Political Text Analysis},
  url = {https://old.gscl.org/media/pages/arbeitskreise/cpss/cpss-2022/workshop-proceedings-2022/254133848-1662996909/cpss-2022-proceedings.pdf},
  year =         2022}

@InProceedings{ceron22:_optim,
  author =       {Tanise Ceron and Nico Blokker and Sebastian Padó},
  title =        {Optimizing text representations to capture (dis)similarity between political parties},
  booktitle = {Proceedings of CoNLL},
  keywords = {conference myown},
  note = {Accepted for publication},
  year =         2022}

@inproceedings{ceron23:_addit,
  abstract = {Automatic extraction of party (dis)similarities from texts such as party election manifestos or parliamentary speeches plays an increasing role in computational political science. How- ever, existing approaches are fundamentally limited to targeting only global party (dis)- similarity: they condense the relationship be- tween a pair of parties into a single figure, their similarity. In aggregating over all policy do- mains (e.g., health or foreign policy), they do not provide any qualitative insights into which domains parties agree or disagree on.
This paper proposes a workflow for estimat- ing policy domain aware party similarity that overcomes this limitation. The workflow cov- ers (a) definition of suitable policy domains; (b) automatic labeling of domains, if no man- ual labels are available; (c) computation of domain-level similarities and aggregation at a global level; (d) extraction of interpretable party positions on major policy axes via mul- tidimensional scaling. We evaluate our work- flow on manifestos from the German federal elections. We find that our method (a) yields high correlation when predicting party similar- ity at a global level and (b) provides accurate party-specific positions, even with automati- cally labelled policy domains.},
  added-at = {2023-05-02T15:14:08.000+0200},
  address = {Toronto, Canada},
  author = {Ceron, Tanise and Nikolaev, Dmitry and Pad{ó}, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2c802a4bcaa362732985cfbe6b4ab376e/sp},
  booktitle = {Findings of ACL},
  interhash = {c15728109a92b2c1f84c603ca1c5249b},
  intrahash = {c802a4bcaa362732985cfbe6b4ab376e},
  keywords = {conference myown},
  timestamp = {2023-07-10T16:07:05.000+0200},
  title = {Additive manifesto decomposition: {A} policy domain aware method for understanding party positioning},
  url = {https://aclanthology.org/2023.findings-acl.499/},
  year = 2023
}

@inproceedings{nikolaev23:_multil,
  abstract = {Scaling analysis is a technique in computational political science that assigns a political actor (e.g. politician or party) a score on a predefined scale based on a (typically long) body of text (e.g. a parliamentary speech or an election manifesto). For example, political scientists have often used the left-right scale to systematically analyse political landscapes of different countries. NLP methods for automatic scaling analysis can find broad application provided they (i) are able to deal with long texts and (ii) work robustly across domains and languages. In this work, we implement and compare two approaches to automatic scaling analysis of political-party manifestos: label aggregation, a pipeline strategy relying on annotations of individual statements from the manifestos, and long-input-Transformer-based models, which compute scaling values directly from raw text. We carry out the analysis of the Comparative Manifestos Project dataset across 41 countries and 27 languages and find that the task can be efficiently solved by state-of-the-art models, with label aggregation producing the best results.},
  added-at = {2023-10-07T22:27:17.000+0200},
  address = {Singapore},
  author = {Nikolaev, Dmitry and Ceron, Tanise and Pad{ó}, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/26b0a2b73c4af8c94ae6add12514d94cc/sp},
  booktitle = {Proceedings of EMNLP},
  interhash = {a4cc641921c9aa1101655d4b42d3a9d2},
  intrahash = {6b0a2b73c4af8c94ae6add12514d94cc},
  keywords = {conference myown},
  note = {To appear},
  timestamp = {2023-10-20T10:36:18.000+0200},
  title = {Multilingual estimation of political-party positioning: From label aggregation to long-input Transformers},
  url = {https://arxiv.org/abs/2310.12575},
  year = 2023
}