Book Reviews: Introduction to Arabic Natural Language Processing by Nizar Y. Habash
By Nizar Habash. International Conference on Computational Logic, 2010
385 citations recorded by the source corpus.
Abstract This book provides system developers and researchers in natural language processing and computational linguistics with the necessary background information for working with the Arabic language. The goal is to introduce Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. The book discusses Arabic script, phonology, orthography, morphology, syntax and semantics, with a final chapter on machine translation issues. The chapter sizes correspond more or less to what is linguistically distinctive about Arabic, with morphology getting the lion's share, followed by Arabic script. No previous knowledge of Arabic is needed. This book is designed for computer scientists and linguists alike. The focus of the book is on Modern Standard Arabic; however, notes on practical issues related to Arabic dialects and languages written in the Arabic script are presented in different chapters. Table of Contents: What is "Arabic"? / Arabic Script / Arabic Phonology and Orthography / Arabic Mo...
View Paper: https://www.semanticscholar.org/paper/2a733deaa7c55e427337860c7643e6b5f8b750fc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Yonatan Belinkov
- Nizar Habash
- Houda Bouamor
- Ahmed El Kholy
- W. Zaghouani
- Siva Reddy
- Filip Ginter
- Arfath Pasha
Citations in the corpus
- CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (2017, result): “For example, in the original tokenization of Arabic, the definite article al- was separated from the modified word, which is comparable to the D3 tokenization scheme (Habash, 2010).”
- MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic (2014, background): “, Alif and Ya variants) (Buckwalter, 2007; Habash, 2010).”
- On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, methodology): “10Arabic examples use the Buckwalter transliteration [48, 140]: http://www.”
- The MADAR Arabic Dialect Corpus and Lexicon (2018, background): “This table relates the typical five-way regional break up of Arabic dialects (Habash, 2010) to a more refined ten-way sub-region division, and even further into 25 cities.”