![]() Similar as stemming, lemmatization reduces inflectional forms to a common base form.It is a process of reducing words to their word stem, base or root form (for example, places - place, watched - watch).Here, Natural Language Toolkit (NLTK) a suite of libraries and programs for symbolic and statistical natural language processing was used to recognize "stop_words". Removes the most common words in a language like “the”, “a”, “on”, “is”, “all” does not carry important meaning.Words, numbers, punctuation marks, and others can be considered as tokens. In this process the given text is splitted into smaller pieces called tokens. ![]() By using strip() function leading and ending spaces are removed.It removes this set of symbols Remove leading and ending whitespaces.Usually, regular expressions are used to remove numbers. Automatically remove numbers if they are not relevant to the analyses.Convert text to lowercase of an input sentence.Here the following techniques were considered. The origin language is english where the translated version is bangla. This script was created to preprocess text that were obtained from several movie subtitles which. Removing stop words, sparse terms, and particular words.Removing punctuations, accent marks and other diacritics.Converting numbers into words or removing numbers.Converting all letters to lower or upper case.Text preprocessing is done by following few steps which are needed for transferring text from human language to machine-readable format. ![]()
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