@InProceedings{glavavs-nanni-ponzetto:2017:NLPandCSS,
  author    = {Glava\v{s}, Goran  and  Nanni, Federico  and  Ponzetto, Simone Paolo},
  title     = {Cross-Lingual Classification of Topics in Political Texts},
  booktitle = {Proceedings of the Second Workshop on NLP and Computational Social Science},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {42--46},
  abstract  = {In this paper, we propose an approach for cross-lingual topical coding of
	sentences from electoral manifestos of political parties in different
	languages. To this end, we exploit continuous semantic text representations and
	induce a joint multilingual semantic vector spaces to enable supervised
	learning using manually-coded sentences across different languages. Our
	experimental results show that classifiers trained on multilingual data yield
	performance boosts over monolingual topic classification.},
  url       = {http://www.aclweb.org/anthology/W17-2906}
}