This is very intuitive, as some of the words that are very unique in nature like names, brands, product names, and some of the noise characters, such as html leftouts, also need to be removed for different NLP tasks. For example, it would be really bad to use names as a predictor for a text classification problem, even if they come out as a significant predictor. We will talk about this further in subsequent chapters. We definitely don't want all these noisy tokens to be present. We also use length of the words as a criteria for removing words with very a short length or a very long length:

>>># tokens is a list of all tokens in corpus
>>>freq_dist = nltk.FreqDist(token)
>>>rarewords = freq_dist.keys()[-50:]
>>>after_rare_words = [ word for word in token not in rarewords]

We are using the FreqDist() function to get the distribution of the terms in the corpus, selecting the rarest one into a list, and then filtering our original corpus. We can also do it for individual documents, as well.