The current interest in accurate dependency parsing make it necessary to build dependency treebanks for French containing both projective and non-projective dependencies. In order to alleviate the work of the annotator, we propose to automatically pre-annotate the sentences with the labels of the dependencies ending on the words. The selection of the dependency labels reduces the ambiguity of the parsing. We show that a maximum entropy Markov model method reaches the label accuracy score of a standard dependency parser (MaltParser). Moreover, this method allows to find more than one label per word, i.e. the more probable ones, in order to improve the recall score. It improves the quality of the parsing step of the annotation process. Therefore, the inclusion of the method in the process of annotation makes the work quicker and more natural to annotators.