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
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Mehmet Uğur Doğan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- 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:
- Cite (ACL):
- Souhir Gahbiche-Braham, Hélène Bonneau-Maynard, Thomas Lavergne, and François Yvon. 2012. Joint Segmentation and POS Tagging for Arabic Using a CRF-based Classifier. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 2107–2113, Istanbul, Turkey. European Language Resources Association (ELRA).
- Cite (Informal):
- Joint Segmentation and POS Tagging for Arabic Using a CRF-based Classifier (Gahbiche-Braham et al., LREC 2012)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2012/pdf/855_Paper.pdf