What is tokenization in NLP?
Welcome to Answerclub.org
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
Sign Up to our social questions and Answers Engine to ask questions, answer people’s questions, and connect with other people.
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
Tokenization is the process of breaking down a sequence of text into smaller units, such as words, subwords, or characters, to facilitate further processing or analysis.
Tokenization is a fundamental step in natural language processing (NLP) that involves breaking down a piece of text into smaller components, known as tokens. These tokens are typically words, phrases, or symbols that carry meaning in the text.
For example, consider the sentence: “The quick brown fox jumps over the lazy dog.”
Tokenizing this sentence would result in the following tokens:
– “The”
– “quick”
– “brown”
– “fox”
– “jumps”
– “over”
– “the”
– “lazy”
– “dog”
Tokenization can vary in complexity based on the specific requirements of the task or language being processed. For instance, it may involve splitting text at whitespace, punctuation marks, or even at the character level. Additionally, tokenization may need to handle special cases like contractions (“can’t” -> [“can”, “‘t”]) or hyphenated words (“well-known” -> [“well”, “-“, “known”]).
Overall, tokenization is a crucial preprocessing step in NLP that enables computers to effectively analyze and understand natural language text.