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Tokenization is a process used in natural language processing (NLP) and information retrieval to break down text into smaller units called tokens. These tokens can be words, phrases, or even individual characters, depending on the specific requirements of the application. The primary goal of tokenization is to simplify the text so that it can be more easily analyzed and processed by algorithms.

The process of tokenization typically involves several steps. First, the text is preprocessed to remove any unnecessary characters or symbols, such as punctuation marks. This helps to ensure that the tokens are clean and consistent. Next, the text is split into individual tokens based on predefined rules or patterns. For example, in English, tokens are often separated by spaces, but in some languages, other delimiters may be used.

Once the text has been tokenized, it can be further processed using various NLP techniques. For instance, tokens can be used to build a vocabulary or to create a bag-of-words model, which is a simple representation of text that counts the frequency of each word. Tokenization is also a crucial step in more advanced NLP tasks, such as part-of-speech tagging, named entity recognition, and machine translation.

In summary, tokenization is a fundamental step in the processing of textual data. It enables the simplification and structuring of text, making it more accessible for further analysis and application in various NLP tasks.