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.

提出了一种基于词的分布式表示（或词嵌入）的独立于语言和领域，完全无监督并可扩展的方法，用于从单词嵌入中学习规范化词典，以修正巴西葡萄牙语产品评论中的拼写错误和互联网俚语，相比当前可用的工具表现更好。