  We propose neural models that can normalize text by considering the
similarities of word strings and sounds. We experimentally compared a model
that considers the similarities of both word strings and sounds, a model that
considers only the similarity of word strings or of sounds, and a model without
the similarities as a baseline. Results showed that leveraging the word string
similarity succeeded in dealing with misspellings and abbreviations, and taking
into account the sound similarity succeeded in dealing with phonetic
substitutions and emphasized characters. So that the proposed models achieved
higher F$_1$ scores than the baseline.
