The document discusses tokenization in Natural Language Processing (NLP), highlighting its function of segmenting input character sequences into tokens, including words, numbers, and punctuation. It describes various types of tokenizers such as whitespace tokenizers, simple tokenizers, and learnable tokenizers. Each tokenizer has distinct methods for identifying tokens based on specific criteria.

1. Tokenization using NLP
- Rakesh

2. Tokenization using NLP
● It will segment an input character sequence into tokens.

3. Tokenization using NLP
● It will segment an input character sequence into tokens.
● Tokens can be words, numbers, punctuations, and etc.

4. Tokenization using NLP
● It will segment an input character sequence into tokens.
● Tokens can be words, numbers, punctuations, and etc.
Example:

5. Types of Tokenizer
● Whitespace Tokenizer - Non whitespace sequences are identified as tokens

6. Types of Tokenizer
● Whitespace Tokenizer - Non whitespace sequences are identified as tokens
● Simple Tokenizer - A character class tokenizer, sequences of the same
character class are tokens

7. Types of Tokenizer
● Whitespace Tokenizer - Non whitespace sequences are identified as tokens
● Simple Tokenizer - A character class tokenizer, sequences of the same
character class are tokens
● Learnable Tokenizer - A maximum entropy tokenizer, detects token
boundaries based on probability model