What is Tokenization?

Tokenization breaks text into smaller parts for easier machine analysis, helping machines understand human language.

Tokenization, in the realm of Natural Language Processing (NLP) and machine Learning, refers to the process of converting a sequence of text into smaller parts, known as tokens. These tokens can be as small as characters or as long as words. The primary reason this process matters is that it helps machines understand human language by breaking it down into bite-sized pieces, which are easier to analyze.

Tokenization Explained

Imagine you're trying to teach a child to read. Instead of diving straight into complex paragraphs, you'd start by introducing them to individual letters, then syllables, and finally, whole words. In a similar vein, tokenization breaks down vast stretches of text into more digestible and understandable units for machines.

The primary goal of tokenization is to represent text in a manner that's meaningful for machines without losing its context. By converting text into tokens, algorithms can more easily identify patterns. This pattern recognition is crucial because it makes it possible for machines to understand and respond to human input. For instance, when a machine encounters the word "running", it doesn't see it as a singular entity but rather as a combination of tokens that it can analyze and derive meaning from.

To delve deeper into the mechanics, consider the sentence, "Chatbots are helpful." When we tokenize this sentence by words, it transforms into an array of individual words:

["Chatbots", "are", "helpful"].

This is a straightforward approach where spaces typically dictate the boundaries of tokens. However, if we were to tokenize by characters, the sentence would fragment into:

["C", "h", "a", "t", "b", "o", "t", "s", " ", "a", "r", "e", " ", "h", "e", "l", "p", "f", "u", "l"].

This character-level breakdown is more granular and can be especially useful for certain languages or specific NLP tasks.

In essence, tokenization is akin to dissecting a sentence to understand its anatomy. Just as doctors study individual cells to understand an organ, NLP practitioners use tokenization to dissect and understand the structure and meaning of text.

It's worth noting that while our discussion centers on tokenization in the context of language processing, the term "tokenization" is also used in the realms of security and privacy, particularly in data protection practices like credit card tokenization. In such scenarios, sensitive data elements are replaced with non-sensitive equivalents, called tokens. This distinction is crucial to prevent any confusion between the two contexts.

Types of Tokenization

Tokenization methods vary based on the granularity of the text breakdown and the specific requirements of the task at hand. These methods can range from dissecting text into individual words to breaking them down into characters or even smaller units. Here's a closer look at the different types:

• Word tokenization. This method breaks text down into individual words. It's the most common approach and is particularly effective for languages with clear word boundaries like English.
• Character tokenization. Here, the text is segmented into individual characters. This method is beneficial for languages that lack clear word boundaries or for tasks that require a granular analysis, such as spelling correction.
• Subword tokenization. Striking a balance between word and character tokenization, this method breaks text into units that might be larger than a single character but smaller than a full word. For instance, "Chatbots" could be tokenized into "Chat" and "bots". This approach is especially useful for languages that form meaning by combining smaller units or when dealing with out-of-vocabulary words in NLP tasks.