Arabic Computational Morphology (eBook, PDF)

Knowledge-based and Empirical Methods
Redaktion: Soudi, Abdelhadi; Neumann, Günter; Bosch, Antal Van Den

• Format: PDF

Produktbeschreibung

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).

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Produktdetails

• Verlag: Springer Netherlands
• Seitenzahl: 308
• Erscheinungstermin: 1. Oktober 2007
• Englisch
• ISBN-13: 9781402060465
• Artikelnr.: 37361671

Autorenporträt

Abdelhadi Soudi, École Nationale de l'Industrie Minérale, Rabat, Morocco / Antal van den Bosch, University Tilburg, The Netherlands / Günter Neumann, Deutsches Forschungszentrum für Künstliche Intelligenz, Saarbrücken, Germany

Inhaltsangabe

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.