Farasa: A Fast and Furious Segmenter for Arabic
2016pp. 11–16
Citations Over TimeTop 1% of 2016 papers
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.
Related Papers
- Study and Two Types of Typical Usage of DataGrid Web Server Control(2005)
- Achieving Parameter of DBSCAN Based on Datagrid(2010)
- Using DataGrid Control to Realize DataBase of Querying in VB6.0(2000)
- Susquehanna Chorale Spring Concert "Roots and Wings"(2017)
- → DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION(2018)