We live in a complex world in which a myriad of seemingly unrelated factors – such as Moore s law which states that the processing capacity on a silicon wafer should increase exponentially, the fall of storage costs and mass adoption of smart-phones contribute to the formation of an increasingly inter-dependent society: 2.5 quintillion bytes of data are generated every day, in fact ninety percent of the world s data were created in the last few years. Harnessing the emerging patterns within the data, effectively separating information from chaos is crucial for both individual decision making as well as for the survival of organizations. In this scenario the best answer from Natural Language Processing researchers is the task of Semantic Role Labeling. SRL is the task the concerns itself with the audacious goal of event understanding, which means determining Who did what to whom, Who was the beneficiary? or What were the means to achieve some goal. APS is also an intermediary task to high level applications such as information extraction, question and answering and chatbots. Traditionally, satisfactory results were obtained only by the introduction of highly specific domain knowledge. For Portuguese, this approach is able to yields a F1 score of 79.6 percent. Recent systems, rely on a pipeline of sub-tasks, yielding a F1 score of 58 percent. In this dissertation, we adopt a new paradigm using recurrent neural networks for the Brazilian Portuguese, that does not rely on a pipeline, our system obtains a score of 66.23 percent.