  Text normalization is an essential task in the processing and analysis of
social media that is dominated with informal writing. It aims to map informal
words to their intended standard forms. Previously proposed text normalization
approaches typically require manual selection of parameters for improved
performance. In this paper, we present an automatic optimizationbased nearest
neighbor matching approach for text normalization. This approach is motivated
by the observation that text normalization is essentially a matching problem
and nearest neighbor matching with an adaptive similarity function is the most
direct procedure for it. Our similarity function incorporates weighted
contributions of contextual, string, and phonetic similarity, and the nearest
neighbor matching involves a minimum similarity threshold. These four
parameters are tuned efficiently using grid search. We evaluate the performance
of our approach on two benchmark datasets. The results demonstrate that
parameter tuning on small sized labeled datasets produce state-of-the-art text
normalization performances. Thus, this approach allows practically easy
construction of evolving domain-specific normalization lexicons
