Part-of-speech tagging is one of the primary stages in natural language processing, providing useful features for performing higher complexity tasks. Word level representations have been largely adopted, either through a conventional sparse codification, such as bag-of-words, or through a distributed representation, like the sophisticated word embedded models used to describe syntactic and semantic information. A central issue on these codifications is the lack of morphological aspects. In addition, recent taggers present per-token accuracies around 97 percent. However, when using a persentence metric, the good taggers show modest accuracies, scoring around 55-57 percent. In this work, we demonstrate how to use n-grams to automatically derive morphological sparse features for text processing. This representation allows neural networks to perform POS tagging from a character-level input. Additionally, we introduce a regularization strategy capable of selecting specific features for each layer unit. As a result, regarding n-grams selection, using the embedded regularization in our models produces two variants. The first one shares globally selected features among all layer units, whereas the second operates individual selections for each layer unit, so that each unit is sensible only to the n-grams that better stimulate it. Using the proposed approach, we generate a high number of features which represent relevant morphosyntactic affection based on a character-level input. Our POS tagger achieves the accuracy of 96.67 percent in the Mac-Morpho corpus for Portuguese.