Year: 2014
Type: conference-paper
Abstract: This paper presents an end-to-end automatic processing system for Arabic.The system performs: correction of common spelling errors pertaining to different forms of alef, ta marbouta and ha, and alef maqsoura and ya; context sensitive word segmentation into underlying clitics, POS tagging, and gender and number tagging of nouns and adjectives.We introduce the use of stem templates as a feature to improve POS tagging by 0.5% and to help ascertain the gender and number of nouns and adjectives.For gender and number tagging, we report accuracies that are significantly higher on previously unseen words compared to a state-of-the-art system.
Authors: Kareem Darwish, Ahmed Abdelali, Hamdy Mubarak
Institution: Qatar Foundation
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2250283392