Souhir Gahbiche-Braham


2014

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Topic Adaptation for the Automatic Translation of News Articles (Adaptation thématique pour la traduction automatique de dépêches de presse) [in French]
Souhir Gahbiche-Braham | Hélène Bonneau-Maynard | François Yvon
Proceedings of TALN 2014 (Volume 1: Long Papers)

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Cross-Lingual Part-of-Speech Tagging through Ambiguous Learning
Guillaume Wisniewski | Nicolas Pécheux | Souhir Gahbiche-Braham | François Yvon
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2012

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Joint Segmentation and POS Tagging for Arabic Using a CRF-based Classifier
Souhir Gahbiche-Braham | Hélène Bonneau-Maynard | Thomas Lavergne | François Yvon
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

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.

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Repérage des entités nommées pour l’arabe : adaptation non-supervisée et combinaison de systèmes (Named Entity Recognition for Arabic : Unsupervised adaptation and Systems combination) [in French]
Souhir Gahbiche-Braham | Hélène Bonneau-Maynard | Thomas Lavergne | François Yvon
Proceedings of the Joint Conference JEP-TALN-RECITAL 2012, volume 2: TALN

2011

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Two Ways to Use a Noisy Parallel News Corpus for Improving Statistical Machine Translation
Souhir Gahbiche-Braham | Hélène Bonneau-Maynard | François Yvon
Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web