Tokenization – Frequently Asked Questions (FAQs)
Q. What is Tokenization in NLP?
Tokenization is the process of converting a sequence of text into smaller parts known as tokens in the context of Natural Language Processing (NLP) and machine learning. These tokens can be as short as a character or as long as a sentence.
Q. What is Lemmatization in NLP?
Lemmatization is a text pre-processing method that helps natural language processing (NLP) models find similarities by reducing a word to its most basic meaning. A lemmatization algorithm, for instance, would reduce the word better to its lemme, or good.
Q. Which are most common types of tokenization?
Word tokenization, which divides text into words, sentence tokenization, which divides text into sentences, subword tokenization, which divides words into smaller units, and character tokenization, which divides text into individual characters, are common forms of tokenization.
NLP | How tokenizing text, sentence, words works
Tokenization in natural language processing (NLP) is a technique that involves dividing a sentence or phrase into smaller units known as tokens. These tokens can encompass words, dates, punctuation marks, or even fragments of words. The article aims to cover the fundamentals of tokenization, it’s types and use case.