Once we have parsed the text from a variety of data sources, the challenge is to make sense of this raw data. Text cleansing is loosely used for most of the cleaning to be done on text, depending on the data source, parsing performance, external noise and so on. In that sense, what we did in Chapter 1, Introduction to Natural Language Processing for cleaning the html using html_clean, can be labeled as text cleansing. In another case, where we are parsing a PDF, there could be unwanted noisy characters, non ASCII characters to be removed, and so on. Before going on to next steps we want to remove these to get a clean text to process further. With a data source like xml, we might only be interested in some specific elements of the tree, with databases we may have to manipulate splitters, and sometimes we are only interested in specific columns. In summary, any process that is done with the aim to make the text cleaner and to remove all the noise surrounding the text can be termed as text cleansing. There are no clear boundaries between the terms data munging, text cleansing, and data wrangling they can be used interchangeably in a similar context. In the next few sections, we will talk about some of the most common pre-processing steps while doing any NLP task.