Arabic Computational Morphology: Knowledge-based and Empirical Methods / Edition 1 available in Hardcover
- Pub. Date:
- Springer Netherlands
The morphology of Arabic poses special challenges to computational natural language processing systems. The exceptional degree of ambiguity in the writing system, the rich morphology, and the highly complex word formation process of roots and patterns all contribute to making computational approaches to Arabic very challenging. Indeed many computational linguists across the world have taken up this challenge over time, and many of the researchers with a track record in this research area have contributed to this book.
The book’s subtitle aims to reflect that widely different computational approaches to the Arabic morphological system have been proposed. These accounts fall into two main paradigms: the knowledge-based and the empirical. Since morphological knowledge plays an essential role in any higher-level understanding and processing of Arabic text, the book also features a part on the role of Arabic morphology in larger applications, i.e. Information Retrieval (IR) and Machine Translation (MT).
Table of Contents
Preface by Richard Sproat
Part 1: Introduction:-Chapter 1: Arabic Computational Morphology, by the editors.-Chapter 2: On Arabic Transliteration, by Nizar Habash, Abdelhadi Soudi and Tim Buckwalter.-Chapter 3: Issues in Arabic Morphology Analysis, by Tim Buckwalter.-Part 2: Knowledge-based methods.-
Chapter 4: A Syllable-based Account of Arabic Morphology, by Lynne Cahill.-Chapter 5: Inheritance-based Approach to Arabic Root-and-Pattern Morphology, by Salah R. Al-Najem.-
Chapter 6: Arabic Morphology Generation, by Violetta Cavalli-Sforza and Abdelhadi Soudi.-
Chapter 7: Grammar-lexis Relations in the Computational Morphology of Arabic, by Joseph Dichy and Ali Farghaly.-Part 3: Empirical Methods:-Chapter 8: Learning to Identify Semitic Roots, by Daya Ezra, Roth Dan and Shuly Wintner.-Chapter 9: Automatic processing of Modern Standard Arabic Text, by Mona Diab, Dan Jurafsky and Kadri Hacioglu.-Chapter 10: Supervised and Unsupervised Learning of Arabic Morphology, by Alexander Clark.-Chapter 11: Memory-based Morphological Analysis and Part-of-Speech Tagging of Arabic, by Antal van den Bosch, Erwin Marsi and Abdelhadi Soudi.-Part 4: Integration of Arabic Morphology in Larger Applications:Chapter 12: Light Stemming for Arabic Information Retrieval, by Leah S. Larkey, Lisa Ballesteros and Margaret E. Connell.-Chapter 13: Adapting Morphology for Arabic Information Retrieval, by Kareem Darwish and Douglas Oard.-Chapter 14: Arabic Morphological Representations for Machine Translation, by Nizar Habash.-Chapter 15: Arabic Morphological Generation and Its Impact on the Quality of Machine Translation to Arabic, by Ahmed Guessoum and Rached Zantout.-Index