on fire)." PUNCT_TO_REMOVE = string.punctuation ans = anslate(str.maketrans('', '', PUNCT_TO_REMOVE)) ans > "It was a great night Shout out to Amy Lee for organizing wonderful event aka on fire" import string text = "It was a great night! Shout out to Lee for organizing wonderful event (a.k.a. String.punctuation in Python (It is the package aforementioned) contains the following items of punctuation. def make_lowercase(token_list): # Assuming word tokenization already happened # Using list comprehension -> loop through every word/token, make it into lower case and add it to a new list words = # join lowercase tokens into one string cleaned_string = " ".join(words) return cleaned_string Remove punctuation The lower function is one of them, and turns all characters into lowercase. The string package (which is a default package in Python) contains various useful functions for strings. This often enables NLP models to perform better by reducing noise in text data. Text cleaning here refers to the process of removing or transforming certain parts of the text so that the text becomes more easily understandable for NLP models that are learning the text. Among these various facets of NLP pre-processing, I will be covering a comprehensive list of text cleaning methods we can apply. In the field of Natural Language Processing (NLP), pre-processing is an important stage where things like text cleaning, stemming, lemmatization, and Part of Speech (POS) Tagging take place. Free for Use Photo from Pexels Introduction
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