Contribution au Niveau de l'Approche Indirecte \`a Base de Transfert dans la Traduction Automatique

TL;DR

This thesis advances Arabic machine translation by developing a morphological analyzer and proposing transfer rules to improve morphosyntactic handling, leading to statistically significant performance gains.

Contribution

It introduces a new Arabic morphological analyzer and a set of transfer rules for English-Arabic translation, enhancing the indirect transfer approach.

Findings

Morphological analyzer showed significant performance improvements.

Transfer rules effectively handled morphosyntactic differences.

Enhanced translation quality demonstrated through validation.

Abstract

In this thesis, we address several important issues concerning the morphological analysis of Arabic language applied to textual data and machine translation. First, we provided an overview on machine translation, its history and its development, then we exposed human translation techniques for eventual inspiration in machine translation, and we exposed linguistic approaches and particularly indirect transfer approaches. Finally, we presented our contributions to the resolution of morphosyntactic problems in computer linguistics as multilingual information retrieval and machine translation. As a first contribution, we developed a morphological analyzer for Arabic, and we have exploited it in the bilingual information retrieval such as a computer application of multilingual documentary. Results validation showed a statistically significant performance. In a second contribution, we proposed a list of morphosyntactic transfer rules from English to Arabic for translation in three phases: analysis, transfer, generation. We focused on the transfer phase without semantic distortion for an abstraction of English in a sufficient subset of Arabic.