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Tokenization

Core Concepts (AI/ML)
The process of splitting text into tokens (words or phrases) during pre-processing.

Tokenization is a fundamental preprocessing step in Natural Language Processing (NLP) where text is broken down into smaller, defined units called tokens. Tokens are typically individual words or phrases.

In LDA, tokenization is performed as part of creating the document corpus. In KeyBERT, a CountVectorizer is used as a tokenizer to split the input text into candidate keywords (N-grams) before they are embedded.

Related Concepts

Semantic Representation Syntax analysis (parsing) N-gram Macro-Context N-gram Matching

Sources & References

Introduction to Machine Learning for SEOs Course
academy.mlforseo.com

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