Dependency parsing is the task that transforms a sentence into a syntactic structure, usually a dependency tree, that represents relations between words. This representations are useful to deal with several tasks that arises with the increasing volume of textual online information and the need for technologies that depends on NLP tasks to work. It can be used, for example, to enable computers to infer the meaning of words of multiple natural languages. This paper presents dependency parsing with focus on one of its most popular modeling in machine learning: the transition-based method. A greedy implementation of this model with a simple neural network-based classifier is used to perform experiments. Universal Dependencies treebanks are used to train and then test the system using the validation script published in the CoNLL-2017 shared task. The results empirically indicate the benefits of initializing the input layer of the network with word embeddings obtained through pre-training. It reached 84.51 LAS in the Portuguese of Brazil test set and 75.19 LAS in the English test set. This result is nearly 4 points behind the performance of the best results of transition-based parsers.