We examine some of the frequently disregarded subtleties of tokenization in Penn Treebank style, and present a new rule-based preprocessing toolkit that not only reproduces the Treebank tokenization with unmatched accuracy, but also maintains exact stand-off pointers to the original text and allows flexible configuration to diverse use cases (e.g. to genreor domain-specific idiosyncrasies). 1 Introduction—Motivation The task of tokenization is hardly counted among the grand challenges of NLP and is conventionally interpreted as breaking up “natural language text [...] into distinct meaningful units (or tokens) ” (Kaplan,