Text Tokenization and Vectorization is a fundamental preprocessing step in natural language processing where raw text is first broken into smaller units called tokens such as words or subwords. These tokens are then converted into numerical representations so that machine learning models can process them effectively. Tokenization helps structure unorganized text, while vectorization transforms it into formats like embeddings or numerical vectors. This process enables algorithms to understand relationships, patterns, and meaning in text data. It is widely used in search systems, chatbots, and machine learning applications. It also improves model accuracy by making text data machine-readable. These techniques form the foundation for most NLP and AI-based text processing tasks.
Showing the single result
