In lexical analysis, tokenization is the process of breaking a stream of text up into words, phrases, symbols, or other meaningful elements called tokens. The list of tokens becomes input for further processing such as parsing or text mining. Tokenization is useful both in linguistics (where it is a form of text segmentation), and in computer science, where it forms part of lexical analysis.
Tokenization is the act of breaking up a sequence of strings into pieces such as words, keywords, phrases, symbols and other elements called tokens. Tokens can be individual words, phrases or even whole sentences. In the process of tokenization, some characters like punctuation marks are discarded. The tokens become the input for another process like parsing and text mining.
Tokenization relies mostly on simple heuristics in order to separate tokens by following a few steps:
Tokens or words are separated by whitespace, punctuation marks or line breaks
White space or punctuation marks may or may not be included depending on the need
All characters within contiguous strings are part of the token. Tokens can be made up of all alpha characters, alphanumeric characters or numeric characters only.
Tokens themselves can also be separators. For example, in most programming languages, identifiers can be placed together with arithmetic operators without white spaces. Although it seems that this would appear as a single word or token, the grammar of the language actually considers the mathematical operator (a token) as a separator, so even when multiple tokens are bunched up together, they can still be separated via the mathematical operator.
Tokenization is used in computer science, where it plays a large part in the process of lexical analysis.
Typically, tokenization occurs at the word level. However, it is sometimes difficult to define what is meant by a "word". Often a tokenizer relies on simple heuristics, for example:
Punctuation and whitespace may or may not be included in the resulting list of tokens.
All contiguous strings of alphabetic characters are part of one token; likewise with numbers.
Tokens are separated by whitespace characters, such as a space or line break, or by punctuation characters.
In languages that use inter-word spaces (such as most that use the Latin alphabet, and most programming languages), this approach is fairly straightforward. However, even here there are many edge cases such as contractions, hyphenated words, emoticons, and larger constructs such as URIs (which for some purposes may count as single tokens). A classic example is "New York-based", which a naive tokenizer may break at the space even though the better break is (arguably) at the hyphen.
Tokenization is particularly difficult for languages written in scriptio continua which exhibit no word boundaries such as Ancient Greek, Chinese, or Thai. Agglutinative languages, such as Korean, also make tokenization tasks complicated.
Some ways to address the more difficult problems include developing more complex heuristics, querying a table of common special-cases, or fitting the tokens to a language model that identifies collocations in a later processing step.
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Tokenization is the act of breaking up a sequence of strings into pieces such as words, keywords, phrases, symbols and other elements called tokens. Tokens can be individual words, phrases or even whole sentences. In the process of tokenization, some characters like punctuation marks are discarded. The tokens become the input for another process like parsing and text mining.
Tokenization relies mostly on simple heuristics in order to separate tokens by following a few steps:
Tokens or words are separated by whitespace, punctuation marks or line breaks
White space or punctuation marks may or may not be included depending on the need
All characters within contiguous strings are part of the token. Tokens can be made up of all alpha characters, alphanumeric characters or numeric characters only.
Tokens themselves can also be separators. For example, in most programming languages, identifiers can be placed together with arithmetic operators without white spaces. Although it seems that this would appear as a single word or token, the grammar of the language actually considers the mathematical operator (a token) as a separator, so even when multiple tokens are bunched up together, they can still be separated via the mathematical operator.
Tokenization is used in computer science, where it plays a large part in the process of lexical analysis.
Typically, tokenization occurs at the word level. However, it is sometimes difficult to define what is meant by a "word". Often a tokenizer relies on simple heuristics, for example:
Punctuation and whitespace may or may not be included in the resulting list of tokens.
All contiguous strings of alphabetic characters are part of one token; likewise with numbers.
Tokens are separated by whitespace characters, such as a space or line break, or by punctuation characters.
In languages that use inter-word spaces (such as most that use the Latin alphabet, and most programming languages), this approach is fairly straightforward. However, even here there are many edge cases such as contractions, hyphenated words, emoticons, and larger constructs such as URIs (which for some purposes may count as single tokens). A classic example is "New York-based", which a naive tokenizer may break at the space even though the better break is (arguably) at the hyphen.
Tokenization is particularly difficult for languages written in scriptio continua which exhibit no word boundaries such as Ancient Greek, Chinese, or Thai. Agglutinative languages, such as Korean, also make tokenization tasks complicated.
Some ways to address the more difficult problems include developing more complex heuristics, querying a table of common special-cases, or fitting the tokens to a language model that identifies collocations in a later processing step.
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