Abstract
In this paper, we present Farasa, a fast and accurate Arabic segmenter. Our approach is based on SVM-rank using linear kernels. We measure the performance of the segmenter in terms of accuracy and efficiency, in two NLP tasks, namely Machine Translation (MT) and Information Retrieval (IR). Farasa outperforms or is at par with the stateof- the-art Arabic segmenters (Stanford and MADAMIRA), while being more than one order of magnitude faster.
Cite
CITATION STYLE
Abdelali, A., Darwish, K., Durrani, N., & Mubarak, H. (2016). Farasa: A fast and furious segmenter for arabic. In NAACL-HLT 2016 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Demonstrations Session (pp. 11–16). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/n16-3003
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
