Arabic Computational Morphology: Knowledge-based and Empirical Methods

Author:   Abdelhadi Soudi ,  Antal van den Bosch ,  Günter Neumann
Publisher:   Springer-Verlag New York Inc.
Edition:   2007 ed.
Volume:   38
ISBN:  

9781402060458


Pages:   308
Publication Date:   11 September 2007
Format:   Hardback
Availability:   In Print   Availability explained
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Arabic Computational Morphology: Knowledge-based and Empirical Methods


Overview

This is the first comprehensive overview of computational approaches to Arabic morphology. The subtitle aims to reflect that widely different computational approaches to the Arabic morphological system have been proposed. The book provides a showcase of the most advanced language technologies applied to one of the most vexing problems in linguistics. It covers knowledge-based and empirical-based approaches.

Full Product Details

Author:   Abdelhadi Soudi ,  Antal van den Bosch ,  Günter Neumann
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   2007 ed.
Volume:   38
Dimensions:   Width: 15.50cm , Height: 1.90cm , Length: 23.50cm
Weight:   1.370kg
ISBN:  

9781402060458


ISBN 10:   1402060459
Pages:   308
Publication Date:   11 September 2007
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Arabic Computational Morphology: Knowledge-based and Empirical Methods.- On Arabic Transliteration.- Issues in Arabic Morphological Analysis.- Knowledge-Based Methods.- A Syllable-based Account of Arabic Morphology.- Inheritance-based Approach to Arabic Verbal Root-and-Pattern Morphology.- Arabic Computational Morphology: A Trade-off Between Multiple Operations and Multiple Stems.- Grammar-Lexis Relations in the Computational Morphology of Arabic.- Empirical Methods.- Learning to Identify Semitic Roots.- Automatic Processing of Modern Standard Arabic Text.- Supervised and Unsupervised Learning of Arabic Morphology.- Memory-based Morphological Analysis and Part-of-speech Tagging of Arabic.- Integration of Arabic Morphology in Larger Applications.- Light Stemming for Arabic Information Retrieval.- Adapting Morphology for Arabic Information Retrieval*.- Arabic Morphological Representations for Machine Translation.- Arabic Morphological Generation and its Impact on the Quality of Machine Translation to Arabic.

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