@inproceedings{glavas-etal-2017-cross,
title = "Cross-Lingual Classification of Topics in Political Texts",
author = "Glava{\v{s}}, Goran and
Nanni, Federico and
Ponzetto, Simone Paolo",
editor = {Hovy, Dirk and
Volkova, Svitlana and
Bamman, David and
Jurgens, David and
O{'}Connor, Brendan and
Tsur, Oren and
Do{\u{g}}ru{\"o}z, A. Seza},
booktitle = "Proceedings of the Second Workshop on {NLP} and Computational Social Science",
month = aug,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2906",
doi = "10.18653/v1/W17-2906",
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.",
}
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%0 Conference Proceedings
%T Cross-Lingual Classification of Topics in Political Texts
%A Glavaš, Goran
%A Nanni, Federico
%A Ponzetto, Simone Paolo
%Y Hovy, Dirk
%Y Volkova, Svitlana
%Y Bamman, David
%Y Jurgens, David
%Y O’Connor, Brendan
%Y Tsur, Oren
%Y Doğruöz, A. Seza
%S Proceedings of the Second Workshop on NLP and Computational Social Science
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F glavas-etal-2017-cross
%X 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.
%R 10.18653/v1/W17-2906
%U https://aclanthology.org/W17-2906
%U https://doi.org/10.18653/v1/W17-2906
%P 42-46
Markdown (Informal)
[Cross-Lingual Classification of Topics in Political Texts](https://aclanthology.org/W17-2906) (Glavaš et al., NLP+CSS 2017)
ACL