Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
BLEU: a method for automatic evaluation of machine translation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Arabic Natural Language Processing
Arabic Natural Language Processing
Segmentation for English-to-Arabic statistical machine translation
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Combining morpheme-based machine translation with post-processing morpheme prediction
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A corpus for modeling morpho-syntactic agreement in Arabic: gender, number and rationality
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Identifying broken plurals, irregular gender, and rationality in Arabic text
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
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We present an approach for generation of morphologically rich languages using statistical machine translation. Given a sequence of lemmas and any subset of morphological features, we produce the inflected word forms. Testing on Arabic, a morphologically rich language, our models can reach 92.1% accuracy starting only with lemmas, and 98.9% accuracy if all the gold features are provided.