Farasa: A fast and furious segmenter for arabic

Ahmed Abdelali, Kareem Darwish, Nadir Durrani, Hamdy Mubarak

Research output: Contribution to conferencePaperpeer-review

296 Citations (Scopus)

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 languageEnglish
Pages11-16
Number of pages6
Publication statusPublished - 2016
Event2016 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 201617 Jun 2016

Conference

Conference2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016
Country/TerritoryUnited States
CitySan Diego
Period12/06/1617/06/16

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