  Text normalization techniques based on rules, lexicons or supervised training
requiring large corpora are not scalable nor domain interchangeable, and this
makes them unsuitable for normalizing user-generated content (UGC). Current
tools available for Brazilian Portuguese make use of such techniques. In this
work we propose a technique based on distributed representation of words (or
word embeddings). It generates continuous numeric vectors of
high-dimensionality to represent words. The vectors explicitly encode many
linguistic regularities and patterns, as well as syntactic and semantic word
relationships. Words that share semantic similarity are represented by similar
vectors. Based on these features, we present a totally unsupervised, expandable
and language and domain independent method for learning normalization lexicons
from word embeddings. Our approach obtains high correction rate of orthographic
errors and internet slang in product reviews, outperforming the current
available tools for Brazilian Portuguese.
