A token is a sequence of characters in text that are used as a unit for analysis. A token may be a word, number, or punctuation mark. For example, the phrase “I am” would be two tokens: “I” and “am.”
Tokens are usually separated by whitespace or other punctuation marks. In some cases, they may be separated by certain formatting conventions, such as italics or bolding.
Tokens are often used in text analytics to measure various linguistic features, such as vocabulary size or the number of times a particular word appears in a text. They can also be used to identify how similar two texts are to one another.
Units of Token
unigram : A unigram is a single token. For example, the word “I” is a unigram.
bigram: A bigram is two tokens that appear consecutively. For example, the bigram “I am” consists of the tokens “I” and “am.”
trigram: A trigram is three tokens that appear consecutively. For example, the trigram “I am a” consists of the tokens “I”, “am”, and “a.”
n-gram: An n-gram is any number of tokens that appear consecutively. For example, a bigram is a 2-gram, and a trigram is a 3-gram.
Types of Tokens
There are two main types of tokens:
words: Words are the most common type of token. They can be any length, but they are typically one to three syllables long.
punctuation marks: Punctuation marks are another common type of token. They include periods, commas, exclamation points, and question marks.
Other types of tokens include numbers, dates, and time stamps, etc.
Uses of Token
Tokens are useful for text analytics because they provide a way to measure various linguistic features, such as vocabulary size or the number of times a particular word appears in a text. They can also be used to identify how similar two texts are to one another. In some cases, tokens can be used to automatically generate summaries of texts.
There are many different ways to tokenize a text, and the choice of how to tokenize a text can have a significant impact on the results of any subsequent analysis. For this reason, it is important to be aware of the different options for tokenization and to choose the one that is best suited for the task at hand.