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We describe an expanded taxonomy of semiotic classes for text normalization, building upon the work in [1]. We add a large number of categories of non-standard words (NSWs) that we believe a robust real-world text normalization system will have to be able to process. Our new categories are based upon empirical findings encountered while building text normalization systems across many languages, for both speech recognition and speech synthesis purposes. We believe our new taxonomy is useful both for ensuring high coverage when writing manual grammars, as well as for eliciting training data to build machine learning-based text normalization systems.