Decoding Text: A Deep Dive into Tokenization Strategies in Natural Language Processing

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Tokenization is a fundamental process in Natural Language Processing (NLP) that involves breaking down a text into smaller units called tokens. Tokens are the building blocks of language and can be words, subwords, or characters, depending on the granularity of the tokenization process. The goal of tokenization is to create a structure that makes it easier to work with and analyze textual data.

I. Key Concepts in Tokenization:

1. Types of Tokens:

• Word Tokens: Represent individual words in a text.
• Subword Tokens: Divide words into smaller units, which can be useful for handling rare words or morphologically rich languages.
• Character Tokens: Treat individual characters as tokens.

2. Tokenization Libraries:

• NLTK (Natural Language Toolkit): A popular library for working with human language data. It provides various tokenization methods.
• spaCy: An industrial-strength NLP library that includes tokenization as part of its processing pipeline.
• Tokenizer in TensorFlow and PyTorch: Deep learning frameworks often have tokenization tools, especially for tasks…