Answering questions formulated in natural language is a long standing quest in Artificial Intelligence. However, even formulating the problem in precise terms has proven to be too challenging, which lead many researchers to focus on Multiple-Choice Question Answering problems. One particularly interesting type of the latter problem is solving standardized tests such as university entrance exams. The Exame Nacional do Ensino Medio (ENEM) is a High School level exam widely used by Brazilian universities as entrance exam, and the world's second biggest university entrance examination in number of registered candidates. In this work we tackle the problem of answering purely textual multiple-choice questions from the ENEM. We build on a previous solution that formulated the problem as a text information retrieval problem. In particular, we investigate how to enhance these methods by text augmentation using Word Embedding and WordNet, a structured lexical database where words are connected according to some relations like synonymy and hypernymy. We also investigate how to boost performance by building ensembles of weakly correlated solvers. Our approaches obtain accuracies ranging from 26% to 29.3%, outperforming the previous approach.