@inproceedings{salaberri-etal-2014-first,
title = "First approach toward Semantic Role Labeling for {B}asque",
author = "Salaberri, Haritz and
Arregi, Olatz and
Zapirain, Be{\~n}at",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/242_Paper.pdf",
pages = "1387--1393",
abstract = "In this paper, we present the first Semantic Role Labeling system developed for Basque. The system is implemented using machine learning techniques and trained with the Reference Corpus for the Processing of Basque (EPEC). In our experiments the classifier that offers the best results is based on Support Vector Machines. Our system achieves 84.30 F1 score in identifying the PropBank semantic role for a given constituent and 82.90 F1 score in identifying the VerbNet role. Our study establishes a baseline for Basque SRL. Although there are no directly comparable systems for English we can state that the results we have achieved are quite good. In addition, we have performed a Leave-One-Out feature selection procedure in order to establish which features are the worthiest regarding argument classification. This will help smooth the way for future stages of Basque SRL and will help draw some of the guidelines of our research.",
}
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%0 Conference Proceedings
%T First approach toward Semantic Role Labeling for Basque
%A Salaberri, Haritz
%A Arregi, Olatz
%A Zapirain, Beñat
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F salaberri-etal-2014-first
%X In this paper, we present the first Semantic Role Labeling system developed for Basque. The system is implemented using machine learning techniques and trained with the Reference Corpus for the Processing of Basque (EPEC). In our experiments the classifier that offers the best results is based on Support Vector Machines. Our system achieves 84.30 F1 score in identifying the PropBank semantic role for a given constituent and 82.90 F1 score in identifying the VerbNet role. Our study establishes a baseline for Basque SRL. Although there are no directly comparable systems for English we can state that the results we have achieved are quite good. In addition, we have performed a Leave-One-Out feature selection procedure in order to establish which features are the worthiest regarding argument classification. This will help smooth the way for future stages of Basque SRL and will help draw some of the guidelines of our research.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/242_Paper.pdf
%P 1387-1393
Markdown (Informal)
[First approach toward Semantic Role Labeling for Basque](http://www.lrec-conf.org/proceedings/lrec2014/pdf/242_Paper.pdf) (Salaberri et al., LREC 2014)
ACL
- Haritz Salaberri, Olatz Arregi, and Beñat Zapirain. 2014. First approach toward Semantic Role Labeling for Basque. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1387–1393, Reykjavik, Iceland. European Language Resources Association (ELRA).