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alexaj

What is Tokenization in NLP, and why is it important?

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Tokenization in NLP is the fundamental process of breaking down text into smaller, more manageable units called tokens. These tokens can be individual words, characters, or even subword units, depending on the specific task and chosen technique.

Here's why tokenization is crucial in NLP:

Makes Text Understandable for Computers: Computers struggle to grasp the nuances of human language. Tokenization breaks down complex sentences into smaller chunks that computers can analyze and process more efficiently.

Foundation for NLP Tasks: Most NLP applications, like sentiment analysis or machine translation, rely on understanding the individual components of a sentence. Tokenization provides the building blocks for further analysis.

Enables Feature Engineering: By separating words or characters, NLP algorithms can identify patterns, word relationships, and other features within the text data. These features are essential for tasks like sentiment analysis or topic modeling.

Prepares Text for Machine Learning Models: Machine learning models typically require numerical data for processing. Tokenization helps convert textual data into a format that machine learning models can understand and work with.

In essence, tokenization acts as the entry point for computers to begin understanding and manipulating human language.

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