In natural language processing, a tokeniser is a tool used to break up text into discrete units called tokens. A token can be a word, punctuation, number, symbol or other meaningful unit in the text. The purpose of the tokeniser is to prepare the text for machine learning analysis and modelling.
There are different types of tokenisers, including rule-based and machine learning-based tokenisers. Rule-based tokenisers use predefined patterns to divide text into tokens, while machine learning-based tokenisers use language models to identify patterns and structures in the text and divide it into tokens.
Tokenisers are an important tool in natural language processing, as proper representation of input data is essential for training accurate machine learning models.
Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
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