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steave
answered Mar 1 '23 00:00
Tokenization is the process of dividing up a text or strings into smaller parts called tokens . The tokens can be words, phrases, or other meaningful units of text. Tokenization is an important step in Natural Language Processing (NLP) because it enables machines to analyze and understand human language.
Tokenization can be done in different ways depending on the specific task and language being analyzed. Here are some common tokenization techniques used in NLP:
Whitespace tokenization : This is the simplest form of tokenization, where text is split into tokens based on whitespace characters such as spaces, tabs, and line breaks.
Word tokenization : In this technique, text is split into words. This can be done using regular expressions to match word patterns, or by using pre-trained models to identify words in the text.
Sentence tokenization : This technique involves dividing a text into separate sentences. This can be done using punctuation marks such as periods, exclamation points, and question marks as delimiters.
Subword tokenization : In some languages, words can be made up of multiple subwords, such as in German where compound words are common. Subword tokenization involves splitting up words into smaller units to improve the performance of machine learning models.
Tokenization is an important first step in NLP because it provides a way to represent text as a sequence of meaningful units that can be processed and analyzed by machines . Once text has been tokenized, other NLP techniques such as part-of-speech tagging, parsing, and sentiment analysis can be applied to gain a deeper understanding of the text.
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