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.
Original language | English |
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Pages | 11-16 |
Number of pages | 6 |
Publication status | Published - 2016 |
Event | 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 - San Diego, United States Duration: 12 Jun 2016 → 17 Jun 2016 |
Conference
Conference | 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016 |
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Country/Territory | United States |
City | San Diego |
Period | 12/06/16 → 17/06/16 |