First approach toward Semantic Role Labeling for Basque

Haritz Salaberri, Olatz Arregi, Beñat Zapirain


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.
Anthology ID:
L14-1230
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1387–1393
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/242_Paper.pdf
DOI:
Bibkey:
Cite (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).
Cite (Informal):
First approach toward Semantic Role Labeling for Basque (Salaberri et al., LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/242_Paper.pdf