HowtogetaChineseName(Entity): segmentation and combination issues
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Combination of Arabic preprocessing schemes for statistical machine translation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Arabic named entity recognition using optimized feature sets
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The impact of morphological stemming on Arabic mention detection and coreference resolution
Semitic '05 Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
Morphology-Based Segmentation Combination for Arabic Mention Detection
ACM Transactions on Asian Language Information Processing (TALIP)
A Cascaded Approach to Mention Detection and Chaining in Arabic
IEEE Transactions on Audio, Speech, and Language Processing
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We investigate in this paper the adequate unit of analysis for Arabic Mention Detection. We experiment different segmentation schemes with various feature-sets. Results show that when limited resources are available, models built on morphologically segmented data outperform other models by up to 4F points. On the other hand, when more resources extracted from morphologically segmented data become available, models built with Arabic TreeBank style segmentation yield to better results. We also show additional improvement by combining different segmentation schemes.