{"id":27340,"date":"2022-04-15T23:44:04","date_gmt":"2022-04-15T18:14:04","guid":{"rendered":"https:\/\/python-programs.com\/?p=27340"},"modified":"2022-04-15T23:44:04","modified_gmt":"2022-04-15T18:14:04","slug":"python-nltk-word_tokenize-function","status":"publish","type":"post","link":"https:\/\/python-programs.com\/python-nltk-word_tokenize-function\/","title":{"rendered":"Python nltk.word_tokenize() Function"},"content":{"rendered":"
NLTK in Python:<\/strong><\/p>\n NLTK is a Python toolkit for working with natural language processing (NLP). It provides us with a large number of test datasets for various text processing libraries. NLTK can be used to perform a variety of tasks such as tokenizing, parse tree visualization, and so on.<\/p>\n Tokenization<\/strong><\/p>\n Tokenization is the process of dividing a large amount of text into smaller pieces\/parts known as tokens. These tokens are extremely valuable for detecting patterns and are regarded as the first stage in stemming and lemmatization. Tokenization also aids in the replacement of sensitive data elements with non-sensitive data elements.<\/p>\n Natural language processing is utilized in the development of applications such as text classification, intelligent chatbots, sentiment analysis, language translation, and so on. To attain the above target, it is essential to consider the pattern in the text.<\/p>\n nltk.word_tokenize() Function:<\/strong><\/p>\n The “nltk.word_tokenize()” method will be used to tokenize sentences and words with NLTK.<\/p>\n NLTK Tokenization is a method of dividing a vast amount of textual data into sections in order to analyze the text’s character.<\/p>\n Tokenization using NLTK can be used for training machine learning models and text cleaning with Natural Language Processing. With NLTK, tokenized words and phrases can be converted into a data frame and vectorized. Tokenization with the Natural Language Tool Kit (NLTK) includes punctuation cleaning, text cleaning, vectorization of parsed text data for better lemmatization, stemming, and machine learning algorithm training.<\/p>\n Kit for Natural Language Processing “tokenize” is a tokenization package in Python Libray. There are two kinds of tokenization functions in the NLTK “tokenize” package.<\/p>\n Syntax:<\/strong><\/p>\n White Space Tokenization, Dictionary Based Tokenization, Rule-Based Tokenization, Regular Expression Tokenization, Penn Treebank Tokenization, Spacy Tokenization, Moses Tokenization, and Subword Tokenization are all advantages of using NLTK for word tokenization. The text normalisation procedure includes all types of word tokenization. The accuracy of language understanding algorithms is improved by normalising the text with stemming and lemmatization. The advantages and benefits of word tokenization with NLTK are listed below.<\/p>\n Approach:<\/strong><\/p>\n Below is the implementation:<\/strong><\/p>\n Output:<\/strong><\/p>\n Approach:<\/strong><\/p>\n Below is the implementation:<\/strong><\/p>\n Output:<\/strong><\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" NLTK in Python: NLTK is a Python toolkit for working with natural language processing (NLP). It provides us with a large number of test datasets for various text processing libraries. NLTK can be used to perform a variety of tasks such as tokenizing, parse tree visualization, and so on. Tokenization Tokenization is the process of …<\/p>\n\n
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word_tokenize(string)<\/pre>\n
Advantages of word tokenization with NLTK<\/h3>\n
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nltk.word_tokenize() Function in Python<\/h2>\n
Method #1: Using word_tokenize() Function (Static Input)<\/h3>\n
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# Import word_tokenize() function from tokenize of the nltk module using the import keyword\r\nfrom nltk.tokenize import word_tokenize\r\n\r\n# Give the string as static input and store it in a variable.\r\ngvn_str = \"hello this is Python-programs welcome all\"\r\n\r\n# Pass the above given string as an argument to the word_tokenize() function to \r\n# tokenize into words and print the result.\r\nprint(word_tokenize(gvn_str))<\/pre>\n
['hello', 'this', 'is', 'Python-programs', 'welcome', 'all']<\/pre>\n
Method #2: Using word_tokenize() Function (User Input)<\/h3>\n
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# Import word_tokenize() function from tokenize of the nltk module using the import keyword\r\nfrom nltk.tokenize import word_tokenize\r\n\r\n# Give the string as user input using the input() function and store it in a variable.\r\ngvn_str = input(\"Enter some random string = \")\r\n\r\n# Pass the above given string as an argument to the word_tokenize() function to \r\n# tokenize into words and print the result.\r\nprint(word_tokenize(gvn_str))<\/pre>\n
Enter some random string = good morning python programs\r\n['good', 'morning', 'python', 'programs']<\/pre>\n