Introduction to Natural Language Processing
Tokenization is the process of breaking down text into individual words or phrases, known as tokens, for further analysis. Tokenization is a critical step in natural language processing, as it allows algorithms to process text data in a structured manner.
The simplest form of tokenization involves splitting text on whitespace, creating tokens from individual words. However, this method can create issues with contractions or punctuation. For example, the sentence 'I don't like apples.' would be tokenized into 'I', 'don't', 'like', and 'apples', which separates the contraction 'don't' into two tokens.
A more advanced tokenization algorithm might use regular expressions to split text on punctuation or combine contractions into a single token.
Tokenization is an essential pre-processing step for many NLP tasks, including sentiment analysis, topic modeling, and machine translation. It can also be used for text cleaning and preparation, removing unwanted characters or formatting from raw text data. There are many tools and libraries available for tokenization, including the Natural Language Toolkit (NLTK) in Python, which provides a suite of functions for different tokenization methods.
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