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.
Fraud detection software is an important tool for protecting companies and individuals from fraudulent activity and minimizing the risk of fraud.
Read More »One of the decisions faced by a company that needs an IT infrastructure is the choice of where to locate this infrastructure and where to install it.
Read More »The semantic web or "internet of knowledge" is an extension of the current web. Unlike the latter, the semantic web is based on proportional [...]
Read More »Cloud computing services or solutions, whether in Spain or anywhere else in the world, are infrastructures, platforms or systems that are used in the cloud.
Read More »
