Tokenized

Tokenized refers to the process of breaking down a sequence of text, such as a sentence or a paragraph, into individual units called tokens. These tokens can be words, punctuation marks, or other meaningful elements. Tokenization is a fundamental step in natural language processing (NLP) and text analysis, enabling computers to understand and process human language by converting it into a structured format. This process involves identifying boundaries, separating elements, and potentially normalizing the text for further analysis like machine learning.

Tokenized meaning with examples

• The first step in sentiment analysis is usually to have the text tokenized. We take a given paragraph of text and separate each word and punctuation mark to create an array of tokens for further evaluation. This allows for a more efficient evaluation on the sentiment of the language.
• Before training a machine learning model to classify emails, the email text must be tokenized. This involves splitting the text into individual words or phrases, cleaning the text, and converting it into a numerical representation to input it into the model.
• To perform a frequency analysis of words in a document, the document's content first has to be tokenized. A program iterates through the text, identifying individual tokens. Each token can then be counted for its frequency, and this data can be used to glean the most common words.
• When building a search engine, the user's query is tokenized to identify key search terms. The search engine then looks up the terms in a pre-tokenized index of web pages, retrieving results relevant to the tokens.
• To facilitate a voice assistant's understanding of a user command, the input speech is transcribed to text and tokenized to isolate the intent and arguments. This aids in the processing of speech and generating an appropriate response.