Year: 2016
Type: conference-paper
Abstract: In this paper, we present Farasa (meaning insight in Arabic), which is a fast and accurate Arabic segmenter.Segmentation involves breaking Arabic words into their constituent clitics.Our approach is based on SVM rank using linear kernels.The features that we utilized account for: likelihood of stems, prefixes, suffixes, and their combination; presence in lexicons containing valid stems and named entities; and underlying stem templates.Farasa outperforms or equalizes state-of-the-art Arabic segmenters, namely QATARA and MADAMIRA.Meanwhile, Farasa is nearly one order of magnitude faster than QATARA and two orders of magnitude faster than MADAMIRA.The segmenter should be able to process one billion words in less than 5 hours.Farasa is written entirely in native Java, with no external dependencies, and is open-source.
Authors: Kareem Darwish, Hamdy Mubarak
Institution: Hamad bin Khalifa University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2575598244