python nlp tokenizer
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Title: Introduction to Natural Language Processing (NLP) Tokenization in Python
Natural Language Processing (NLP) involves the interaction between computers and human language. Tokenization is a fundamental step in NLP, where a text is broken down into smaller units, called tokens. Tokens can be words, phrases, or even characters, depending on the level of granularity required. In this tutorial, we'll explore the basics of NLP tokenization in Python using the popular library NLTK (Natural Language Toolkit).
Make sure you have Python installed on your machine. You can install the NLTK library using the following command:
NLTK provides a variety of tokenizers to cater to different needs. For this tutorial, we'll focus on word and sentence tokenization.
Word tokenization involves breaking a text into individual words. Let's look at an example:
In this example, we use the word_tokenize function from NLTK to tokenize the given text into individual words. The output will be a list of words.
Sentence tokenization, on the other hand, involves breaking a text into individual sentences. Here's an example:
In this example, we use the sent_tokenize function to tokenize the given text into sentences. The output will be a list of sentences.
Tokenization is a crucial preprocessing step in NLP, providing the foundation for various language processing tasks. NLTK offers a user-friendly interface for tokenization, making it accessible for both beginners and experienced developers. Experiment with different texts and tokenizers to get a better understanding of how tokenization works in the context of natural language processing.
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