@inproceedings{perez-rosas-etal-2012-learning,
title = "Learning Sentiment Lexicons in {S}panish",
author = "P{\'e}rez-Rosas, Ver{\'o}nica and
Banea, Carmen and
Mihalcea, Rada",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Do{\u{g}}an, Mehmet U{\u{g}}ur and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/1081_Paper.pdf",
pages = "3077--3081",
abstract = "In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90{\%}) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74{\%}) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66{\%} in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources.",
}
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%0 Conference Proceedings
%T Learning Sentiment Lexicons in Spanish
%A Pérez-Rosas, Verónica
%A Banea, Carmen
%A Mihalcea, Rada
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Doğan, Mehmet Uğur
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12)
%D 2012
%8 May
%I European Language Resources Association (ELRA)
%C Istanbul, Turkey
%F perez-rosas-etal-2012-learning
%X In this paper we present a framework to derive sentiment lexicons in a target language by using manually or automatically annotated data available in an electronic resource rich language, such as English. We show that bridging the language gap using the multilingual sense-level aligned WordNet structure allows us to generate a high accuracy (90%) polarity lexicon comprising 1,347 entries, and a disjoint lower accuracy (74%) one encompassing 2,496 words. By using an LSA-based vectorial expansion for the generated lexicons, we are able to obtain an average F-measure of 66% in the target language. This implies that the lexicons could be used to bootstrap higher-coverage lexicons using in-language resources.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/1081_Paper.pdf
%P 3077-3081
Markdown (Informal)
[Learning Sentiment Lexicons in Spanish](http://www.lrec-conf.org/proceedings/lrec2012/pdf/1081_Paper.pdf) (Pérez-Rosas et al., LREC 2012)
ACL
- Verónica Pérez-Rosas, Carmen Banea, and Rada Mihalcea. 2012. Learning Sentiment Lexicons in Spanish. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 3077–3081, Istanbul, Turkey. European Language Resources Association (ELRA).