@InProceedings{glavavs-nanni-ponzetto:2017:EACLshort,
  author    = {Glava\v{s}, Goran  and  Nanni, Federico  and  Ponzetto, Simone Paolo},
  title     = {Unsupervised Cross-Lingual Scaling of Political Texts},
  booktitle = {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},
  publisher = {Association for Computational Linguistics},
  pages     = {688--693},
  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.},
  url       = {http://www.aclweb.org/anthology/E17-2109}
}