![]() ![]() Basically the larger the polynomial the less efficient the algorithm, in this case O(n*m) is larger than O(n + m) so the list_clean method is theoretically less efficient than the set_clean method. The O(n*m) and O(n + m) are examples of big o notation, a theoretical approach of measuring the efficiency of algorithms. For the given example the set_clean is almost 10 times faster. The first time corresponds to list_clean and the second time corresponds to set_clean. Nltk clean text code#In the code above list_clean is a function that removes stopwords using a list and set_clean is a function that removes stopwords using a set. Print(timeit.timeit('set_clean(text)', 'from _main_ import text,set_clean', number=5)) Print(timeit.timeit('list_clean(text)', 'from _main_ import text,list_clean', number=5)) Set_stop_words = set(stopwords.words('english')) ![]() Let's compare both approaches list vs set: import timeit Using a list, your approach is O(n*m) where n is the number of words in text and m is the number of stop-words, using a set the approach is O(n + m). file in the stopwords directory.Try converting stopwords to a set. Note: You can even modify the list by adding words of your choice in the english. To check the list of stopwords you can type the following commands in the python shell. home/pratima/nltk_data/corpora/stopwords is the directory address.(Do not forget to change your home directory name) ![]() You can find them in the nltk_data directory. One can think and compare among various variants of outputs. This library offers a lot of algorithms that helps significantly in the learning purpose. It does work: > 'with dot.'.translate (None, string.punctuation) 'with dot' (note no dot at the end of the result) It may cause problems if you have things like 'end of sentence.No space', in which case do. A Comprehensive Guide on Text Cleaning Using the nltk Library NLTK is a library that processes on string input and output’s the result in the form of either a string or lists of strings. NLTK(Natural Language Toolkit) in python has a list of stopwords stored in 16 different languages. To get rid of the punctuation, you can use a regular expression or python's isalnum () function. For this, we can remove them easily, by storing a list of words that you consider to stop words. We would not want these words to take up space in our database, or taking up valuable processing time. Stop Words: A stop word is a commonly used word (such as “the”, “a”, “an”, “in”) that a search engine has been programmed to ignore, both when indexing entries for searching and when retrieving them as the result of a search query.
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