Grammar-Lexis Relations in the Computational Morphology of Arabic

  • Joseph Dichy
  • Ali Farghaly
Part of the Text, Speech and Language Technology book series (TLTB, volume 38)


Grammar-lexis rules and relations ensuring correct insertion of major lexical entries (nouns, verbs and deverbals) play an essential part in the computational morphology of Arabic. This chapter, which is based on the experiences of the DIINAR.1 Arabic lexical resource and related software, and on that of the first version of the SYSTRAN Arabic-English MT system, outlines previous approaches of the computational morphology of the language (Section 2): root and pattern (shortly recalled); lexeme-based; machine learning and statistical; stems, based on roots and patterns, and finally, the stem-based approach, including root and pattern as well as grammar-lexis information. The latter, which is the most compliant to the requirements of machine-translation and other high-level applications, is further developed in Section 3 of the Arabic word-form and a mapping of rules and relations accounting for grammar-lexis relations operating within the boundaries of that complex unit. In the Word-Formatives Grammar, rules and relations involving the lexical nucleus of the word-form play a crucial part and are formalised in a computational perspective. The stem either coincides with, or is the core of the nucleus, because lexical entries include two overall categories: in the first, stem and entry coincide; in the second, the lexical entry corresponds to a morphological compound encompassing the stem and a lexicalized extension (in most cases, a suffix which is part of the entry). Correct relations between the lexical nucleus and the other formatives included in the word-form are ensured through morphosyntactic specifiers associated to each entry of the lexical database. These relations, which have been included in the DIINAR.1 database, are both finite in number and exhaustive in coverage. They also allow computational morphology and other applications to rely on a good restriction of the generated lexica: only cliticized or affixed formatives that can effectively be associated with a given lexical nucleus are added, and ‘illegal’ ones are ruled out. In the DIINAR.1 resource, the effective number of inflected word-forms is 7,774,938 (about nine times less than one would obtain through ‘blind’ generation). A comprehensive mapping of examples is given. Their compatibility with applications going beyond computational morphology is also outlined


Machine Translation Lexical Entry Nucleus Formative Statistical Machine Translation Parallel Corpus 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer 2007

Authors and Affiliations

  • Joseph Dichy
    • 1
  • Ali Farghaly
    • 2
  1. 1.Université Lumière-Lyon 2ICAR research lab (CNRS/Lyon 2)69365 Lyon Cedex 07France
  2. 2.Oracle USARedwood ShoresUSA

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