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.- Anthology ID:
- W17-2906
- Volume:
- Proceedings of the Second Workshop on NLP and Computational Social Science
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Dirk Hovy, Svitlana Volkova, David Bamman, David Jurgens, Brendan O’Connor, Oren Tsur, A. Seza Doğruöz
- Venue:
- NLP+CSS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 42–46
- Language:
- URL:
- https://aclanthology.org/W17-2906
- DOI:
- 10.18653/v1/W17-2906
- Cite (ACL):
- Goran Glavaš, Federico Nanni, and Simone Paolo Ponzetto. 2017. Cross-Lingual Classification of Topics in Political Texts. In Proceedings of the Second Workshop on NLP and Computational Social Science, pages 42–46, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Lingual Classification of Topics in Political Texts (Glavaš et al., NLP+CSS 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W17-2906.pdf