Machine Translation is one of the most difficult tasks in natural language and speech processing. The linguistic peculiarities of some languages makes the machine translation task more difficult. In this thesis, we present a detailed study of machine translation systems from arabic to french and to english.Our principle researches carry on building parallel corpora, arabic preprocessing and adapting translation and language models. We propose a method for automatic extraction of parallel news corpora from a comparable corpora. Two approaches for translation model adaptation are explored using whether parallel corpora extracted automatically or parallel corpora constructed automatically. We demonstrate that adapting data used to build machine translation system improves translation.Arabic texts have to be preprocessed before machine translation and this because of the agglutinative character of arabic language. A prepocessing tool for arabic, SAPA (Segmentor and Part-of-speech tagger for Arabic), much faster than the state of the art tools and totally independant of any other external resource was developed. This tool predicts simultaneously morphosyntactic tags and proclitics (conjunctions, prepositions, etc.) for every word, then splits off words into lemma and proclitics.We describe also in this thesis, our named entity recognition tool for arabic, NERAr, and we focus on the impact of integrating named entity recognition in the preprocessing task. We used bilingual dictionaries to propose translations of the detected named entities. We present then many approaches to adapt thematically translation and language models using a corpora consists of a set of multicategoric sentences.These experiments open important research perspectives such as combining many systems when translating. It would be interesting also to focus on a temporal adaptation of translation and language models.Finally, improved machine translation systems from arabic to french and english are integrated in a multimedia platform analysis and shows improvements compared to basic machine translation systems.
