Why and how do we tokenize a text?

Why Tokenize Text?

Tokenization is a crucial preprocessing step in natural language processing (NLP) that involves splitting text into smaller units, called tokens. Tokens can be words, phrases, or other meaningful units of text, depending on the specific task and requirements. Tokenization serves several purposes:

1. Text Analysis: Tokenization enables computers to process and analyze text data more effectively by breaking it down into smaller, manageable units. This allows for easier manipulation, computation, and extraction of information from the text.
2. Feature Extraction: In NLP tasks such as sentiment analysis, document classification, and named entity recognition, tokens serve as features or input variables for machine learning models. Tokenization helps extract relevant features from the text that can be used to train predictive models.
3. Text Normalization: Tokenization is often a precursor to text normalization steps such as stemming and lemmatization. By tokenizing text into individual units, it becomes easier to apply normalization techniques to each token separately.
4. Information Retrieval: In search engines and information retrieval systems, tokenization is used to index and retrieve documents based on specific keywords or terms. Tokenized text forms the basis for building inverted indexes that facilitate efficient searching and retrieval of relevant documents.

How to Tokenize Text?

There are various approaches to tokenizing text, depending on the specific requirements of the task and the characteristics of the text data. Some common methods of tokenization include:

1. Word Tokenization: This is the most common type of tokenization, where text is split into individual words. Word tokenization can be performed using whitespace as a delimiter or more advanced techniques such as regular expressions or natural language processing libraries.
2. Sentence Tokenization: Sentence tokenization involves splitting text into individual sentences. This is particularly useful for tasks that require analyzing or processing text on a sentence-by-sentence basis, such as machine translation or summarization.

Types of Tokenization:

1. Word Tokenization:
   • A piece of text is divided into individual words. For example:
     • Sentence: “The quick brown fox jumps over the lazy dog”
     • Tokens: “The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”
2. Sentence Tokenization:
   • This technique involves breaking down a piece of text into individual sentences. For example:
     • Paragraph: “The quick brown fox jumps over the lazy dog. It was a sunny day. The fox was very happy.”
     • Tokens: “The quick brown fox jumps over the lazy dog.”, “It was a sunny day.”, “The fox was very happy.”
3. N-gram Tokenization:
   • N-gram tokenization involves creating contiguous sequences of words from a piece of text.
     • Paragraph: “The quick brown fox jumps over the lazy dog”
     • Token bi-gram: “The quick”, “quick brown”, “brown fox”, “fox jumps”, “jumps over”, “over the”, “the lazy”, “lazy dog”.
4. Stemming:
   • Stemming is a type of tokenization that involves reducing a word to its base form, or stem. For example:
     • The stem of the word “jumps” is “jump”.
     • The stem of the word “jumping” is also “jump”.
5. Lemmatization:
   • Lemmatization is used in NLP because it can produce more meaningful and accurate tokens than stemming. For example:
     • The lemma of the verb “jumps” is “jump”.
     • The lemma of the noun “jumps” is “jump”.
6. White Space Tokenization:
   • This technique involves dividing a piece of text into tokens based on white space characters, such as spaces, tabs, and newline characters. For example:
     • Sentence: “The quick brown fox jumps over the lazy dog”
     • Word tokens: “The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”.
7. Punctuation Tokenization:
   • This technique involves dividing a piece of text into tokens based on punctuation marks, such as periods, commas, and exclamation points. For example:
     • Sentence: “The quick brown fox jumps over the lazy dog!”
     • Tokens: “The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”, “!”