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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.
Methods and obstacles
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:
- 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.
- Punctuation and whitespace may or may not be included in the resulting list of tokens.
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.
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.
- Apache OpenNLP includes rule based and statistical tokenizers which support many languages
- U-Tokenizer is an API over HTTP that can cut Mandarin and Japanese sentences at word boundary. English is supported as well.
- RoboVerdict is the implementation of an algorithm that automatically rates products by tokenizing texts of various reviews and finding similarities between them. English is the only supported language.
- Huang, C., Simon, P., Hsieh, S., & Prevot, L. (2007)Rethinking Chinese Word Segmentation: Tokenization, Character Classification, or Word break Identification
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