Joint Segmentation and POS Tagging for Arabic Using a CRF-based Classifier

Souhir Gahbiche-Braham, Hélène Bonneau-Maynard, Thomas Lavergne, François Yvon


Abstract
Arabic is a morphologically rich language, and Arabic texts abound of complex word forms built by concatenation of multiple subparts, corresponding for instance to prepositions, articles, roots prefixes, or suffixes. The development of Arabic Natural Language Processing applications, such as Machine Translation (MT) tools, thus requires some kind of morphological analysis. In this paper, we compare various strategies for performing such preprocessing, using generic machine learning techniques. The resulting tool is compared with two open domain alternatives in the context of a statistical MT task and is shown to be faster than its competitors, with no significant difference in MT quality.
Anthology ID:
L12-1509
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
2107–2113
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/855_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/855_Paper.pdf