One of the most important tasks in Natural Language Processing is syntactic parsing, where the structure of a sentence is inferred according to a given grammar. Syntactic parsing, thus, tells us how to determine the meaning of the sentence fromthemeaning of the words in it. Syntactic parsing based on dependency grammars is called dependency parsing. The Dependency-based syntactic parsing task consists in identifying a head word for each word in an input sentence. Hence, its output is a rooted tree, where the nodes are the words in the sentence. This simple, yet powerful, structure is used in a great variety of applications, like Question Answering,Machine Translation, Information Extraction and Semantic Role Labeling. State-of-the-art dependency parsing systems use transition-based or graph-based models. This dissertation presents a token classification approach to dependency parsing, by creating a special tagging set that helps to correctly find the head of a token. Using this tagging style, any classification algorithm can be trained to identify the syntactic head of each word in a sentence. In addition, this classification model treats projective and non-projective dependency graphs equally, avoiding pseudo-projective approaches. To evaluate its effectiveness, we apply the Entropy Guided Transformation Learning algorithm to the publicly available corpora from the CoNLL 2006 Shared Task. These computational experiments are performed on three corpora in different languages, namely: Danish, Dutch and Portuguese. We use the Unlabelled Attachment Score as the accuracy metric. Our results show that the generated models are above the average CoNLL system performance. Additionally, these findings also indicate that the token classification approach is a promising one.