@inproceedings{li-etal-2012-annotating,
title = "Annotating Opinions in {G}erman Political News",
author = "Li, Hong and
Cheng, Xiwen and
Adson, Kristina and
Kirshboim, Tal and
Xu, Feiyu",
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/640_Paper.pdf",
pages = "1183--1188",
abstract = "This paper presents an approach to construction of an annotated corpus for German political news for the opinion mining task. The annotated corpus has been applied to learn relation extraction rules for extraction of opinion holders, opinion content and classification of polarities. An adapted annotated schema has been developed on top of the state-of-the-art research. Furthermore, a general tool for annotating relations has been utilized for the annotation task. An evaluation of the inter-annotator agreement has been conducted. The rule learning is realized with the help of a minimally supervised machine learning framework DARE.",
}
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<abstract>This paper presents an approach to construction of an annotated corpus for German political news for the opinion mining task. The annotated corpus has been applied to learn relation extraction rules for extraction of opinion holders, opinion content and classification of polarities. An adapted annotated schema has been developed on top of the state-of-the-art research. Furthermore, a general tool for annotating relations has been utilized for the annotation task. An evaluation of the inter-annotator agreement has been conducted. The rule learning is realized with the help of a minimally supervised machine learning framework DARE.</abstract>
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%0 Conference Proceedings
%T Annotating Opinions in German Political News
%A Li, Hong
%A Cheng, Xiwen
%A Adson, Kristina
%A Kirshboim, Tal
%A Xu, Feiyu
%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 li-etal-2012-annotating
%X This paper presents an approach to construction of an annotated corpus for German political news for the opinion mining task. The annotated corpus has been applied to learn relation extraction rules for extraction of opinion holders, opinion content and classification of polarities. An adapted annotated schema has been developed on top of the state-of-the-art research. Furthermore, a general tool for annotating relations has been utilized for the annotation task. An evaluation of the inter-annotator agreement has been conducted. The rule learning is realized with the help of a minimally supervised machine learning framework DARE.
%U http://www.lrec-conf.org/proceedings/lrec2012/pdf/640_Paper.pdf
%P 1183-1188
Markdown (Informal)
[Annotating Opinions in German Political News](http://www.lrec-conf.org/proceedings/lrec2012/pdf/640_Paper.pdf) (Li et al., LREC 2012)
ACL
- Hong Li, Xiwen Cheng, Kristina Adson, Tal Kirshboim, and Feiyu Xu. 2012. Annotating Opinions in German Political News. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 1183–1188, Istanbul, Turkey. European Language Resources Association (ELRA).