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Tokenization and vectorization are two key processes in natural language processing (NLP) that help convert human-readable text into a form that machines can process and understand.

1. Tokenization:

Tokenization is the process of breaking down a sentence, text, or any data into smaller components, typically words or subwords, called tokens. These tokens are the building blocks that algorithms use to understand the text.

For example:

• Sentence: "I love coding."
• After tokenization: ["I", "love", "coding", "."]

In more advanced cases, tokenization may break words into subwords or even characters, depending on the needs of the model. For instance, the word "coding" might be split into smaller components like ["cod", "ing"].

2. Vectorization:

Vectorization is the process of converting these tokens into numerical representations (vectors) that algorithms can process mathematically.

• Each token (word or subword) is mapped to a vector of numbers. These vectors capture semantic meaning, allowing similar words to have similar vectors.

There are several methods to vectorize text:

• One-hot encoding: Each word is represented as a binary vector, where only one index is "1" and the rest are "0". However, this method is less efficient for larger datasets due to its sparse nature.
• TF-IDF: This method assigns a value to each word based on its frequency in the document and how unique it is across documents.
• Word embeddings: Pre-trained models like Word2Vec or GloVe convert tokens into dense vectors where semantically similar words have closer vectors.
• Contextual embeddings: Modern models like GPT or BERT generate vectors based on the context of the word in a sentence, providing more nuanced representations.

Why are tokenization and vectorization important?

These processes allow AI systems to interpret and work with language data. For example, when searching for related products in your system, the query and product descriptions need to be tokenized and vectorized. Once represented as vectors, the system can calculate the similarity between a query and the products using mathematical operations.