Abstract
Political text scaling aims to linearly order parties and politicians across political dimensions (e.g., left-to-right ideology) based on textual content (e.g., politician speeches or party manifestos). Existing models scale texts based on relative word usage and cannot be used for cross-lingual analyses. Additionally, there is little quantitative evidence that the output of these models correlates with common political dimensions like left-to-right orientation. Experimental results show that the semantically-informed scaling models better predict the party positions than the existing word-based models in two different political dimensions. Furthermore, the proposed models exhibit no drop in performance in the cross-lingual compared to monolingual setting.- Anthology ID:
- E17-2109
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 688–693
- Language:
- URL:
- https://aclanthology.org/E17-2109
- DOI:
- Cite (ACL):
- Goran Glavaš, Federico Nanni, and Simone Paolo Ponzetto. 2017. Unsupervised Cross-Lingual Scaling of Political Texts. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 688–693, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Cross-Lingual Scaling of Political Texts (Glavaš et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-2109.pdf
- Code
- gg42554/cl-scaling