In this paper we describe a lexical disambiguation algorithm based on a statistical language model we call maximum likelihood disambiguation. The maximum likelihood method depends solely on the target language. The model was trained on a corpus of American English newspaper texts. Its performance was tested using output from a transfer based translation system between Turkish and English. The method is source language independent, and can be used for systems translating from any language into English.

In this paper we describe a language recognition algorithm for multilingual documents that is based on mixed-order n-grams, Markov chains, maximum likelihood, and dynamic programming. We present the results of an experimental study that showed that the performance of this algorithm has practical value.