Machine translation engines draw on various types of databases. This paper is concerned with Arabic as a source or target language, and focuses on lexical databases. The non-concatenative nature of Arabic morphology, the complex structure of Arabic word-forms, and the general use of vowel-free writing present a real challenge to NLP developers. We show here how and why a stem-grounded lexical database, the items of which are associated with grammar-lexis specifications – as opposed to a root-&-pattern database –, is motivated both linguistically and with regards to efficiency, economy and modularity. Arguments in favour of databases relying on stems associated with grammar-lexis specifications (such as DIINAR.1 or the Arabic dB under development at SYSTRAN), rather than on roots and patterns, are the following: (a) The latter include huge numbers of rule-generated word-forms, which do not actually appear in the language. (b) Rule-generated lemmas – as opposed to existing ones – are widely under-specified with regards to grammar-lexis relations. (c) In a Semitic language such as Arabic, the mapping of grammar-lexis specifications that need to be associated with every lexical entry of the database is decisive. (d) These specifications can only be included in a stem-based dB. Points (a) to (d) are crucial and in the context of machine translation involving Arabic.

SYSTRAN started the design and the development of Arabic, Farsi and Urdu to English machine translation systems in July 2002. This paper describes the methodology and implementation adopted for dictionary building and morphological analysis. SYSTRAN’s IntuitiveCoding® technology (ICT) for facilitates the creation, update, and maintenance of Arabic, Farsi and Urdu lexical entries, is more modular and less costly. ICT for Arabic, Farsi, and Urdu requires the implementation of stem-based lexical entries, the authentic scripts for each language, a statistical Arabic stem-guesser, and separate declarative modules for internal and external morphology.