Nous n'avons pas encore la date officielle de parutionInternational audienceLanguage modeling is a very important step in several NLP applications. Most of the current language models are based on probabilistic methods. In this paper, we propose a new language modeling approach based on the possibility theory. Our goal is to suggest a method for estimating the possibility of a word-sequence and to test this new approach in a machine translation system.We propose a word-sequence possibilistic measure, which can be estimated from a corpus. We proceeded in two ways: first, we checked the behaviour of the newapproach compared with the existing work. Second, we compared the new language model with the probabilistic one used in statistical MTsystems. The results, in terms of the METEOR metric, show that the possibilistic-language model is better than the probabilistic one. However, in terms of BLEU and TER scores, the probabilistic model remains better
