Token

See also: Machine learning terms

Introduction

In the field of machine learning, a token refers to a fundamental unit of text or data that is used for processing, analysis, or modeling. Tokens are essential components of natural language processing (NLP) systems, which aim to enable computers to understand, interpret, and generate human language. In this context, a token can represent a single word, a character, a subword, or any other unit of text that serves as an input for a given NLP model. The process of breaking down a given text into its constituent tokens is known as tokenization.

Tokenization Techniques

Tokenization is a crucial step in the preprocessing of textual data for various machine learning tasks, such as text classification, sentiment analysis, and machine translation. There are several techniques employed for tokenization, each with its own set of advantages and disadvantages.

Word-based Tokenization

Word-based tokenization is a common approach that involves segmenting text into individual words, treating each word as a separate token. This technique often uses white spaces and punctuation marks as delimiters. While it is relatively simple and intuitive, word-based tokenization may face challenges when dealing with languages that do not use spaces between words or when handling morphologically rich languages, where a single word can convey multiple pieces of information.

Character-based Tokenization

Character-based tokenization divides text into individual characters, which are then used as tokens. This approach can be useful when dealing with languages that lack clear word boundaries, such as Chinese or Japanese. However, character-based tokenization may lead to an increased number of tokens, making it computationally expensive and prone to overfitting in some machine learning tasks.

Subword Tokenization

Subword tokenization is a more advanced technique that combines the benefits of both word-based and character-based tokenization. It involves breaking text into smaller units, known as subwords or subtokens, which are often derived from frequently occurring character sequences. Examples of subword tokenization algorithms include Byte-Pair Encoding (BPE) and WordPiece. These methods have proven effective in addressing issues such as out-of-vocabulary words and handling morphologically complex languages.

Applications of Tokens in Machine Learning

Tokens play a crucial role in various machine learning tasks, particularly in the domain of NLP. Some of the primary applications include:

• Text Classification: Tokens are used to represent textual data in a format that can be easily processed by machine learning algorithms for tasks such as sentiment analysis, topic classification, and spam detection.
• Machine Translation: In machine translation systems, tokens are used to represent source and target language texts, enabling the model to learn mappings between different languages.
• Named Entity Recognition: Tokenization allows for the identification of words or phrases that represent specific entities, such as people, organizations, or locations, in a given text.

Explain Like I'm 5 (ELI5)

Imagine you have a box of toy blocks with letters on them. If you want to build a sentence or a phrase, you need to use these blocks (letters) to make words, and then put those words together to create sentences. In machine learning, a token is like a block (a word or a letter) that helps the computer understand and process the text. To make it easier for the computer to work with text, we break the text into smaller pieces called tokens, which can be words, characters, or other smaller parts of the text. This helps the computer learn how to understand and create sentences, just like how you use the toy blocks to build phrases and sentences.