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this video covers text ization ill discuss some common terminology and challenges in text ization and then ill show a simple example of how to ize text using basic python commands regular expressions and existing nlp libraries text ization is a critical first step for most natural language processing tasks typically when processing text youll start by running a fairly standard nlp pipeline that includes a izer as well as usually some other tools like part of speech tigers which well learn about later this semester an nlp pipeline generally starts by separating words and running text or izing them itll also normalize them so for example if there are both british and american english spellings it can switch everything to one or the other and then itll segment the sentences which can be really useful for defining contextual boundaries initially it might seem straightforward to ize text but there are actually a lot of gray areas for example in the sentence here how many words are the
