Kleene 1951; 1956 first defined regular expressions and the finite automaton, based on the McCulloch-Pitts neuron. Ken Thompson was one of the first to build regular expressions compilers into editors for text searching (Thompson, 1968) . His editor ed included a command "g/regular expression/p", or Global Regular Expression Print, which later became the Unix grep utility. Text normalization algorithms have been applied since the beginning of the field. One of the earliest widely used stemmers was Lovins (1968) . Stemming was also applied early to the digital humanities, by Packard (1973) , who built an affix-stripping morphological parser for Ancient Greek. Currently a wide variety of code for tokenization and normalization is available, such as the Stanford Tokenizer (http://nlp.stanford.edu/software/tokenizer.shtml) or specialized tokenizers for Twitter (O'Connor et al., 2010) , or for sentiment (http: //sentiment.christopherpotts.net/tokenizing.html). See Palmer (2012) for a survey of text preprocessing. NLTK is an essential tool that offers both useful Python libraries (http://www.nltk.org) and textbook descriptions (Bird et al., 2009 ) of many algorithms including text normalization and corpus interfaces.