Tokenization concept description

Submitted by admin on Wed, 08/02/2023 - 15:00

Tokenization is the process of breaking a document or a piece of text into smaller units called tokens. 

In the context of natural language processing (NLP) and computational linguistics, a token typically represents a word, punctuation mark, or any other meaningful subunit of the text. 

For example, consider the following sentence: "Tokenization is an important step in NLP!" After tokenization, the sentence may be broken down into individual tokens as follows: 








As you can see, each word is treated as a separate token, and even the exclamation mark is considered a separate token. Tokenization is a critical preprocessing step in NLP tasks as it helps convert raw text data into a format that can be easily processed by algorithms and models. 

Tokenization serves several purposes: 

Text representation: Tokenization converts raw text into a sequence of tokens that can be further processed or used as input for various NLP tasks. 

Vocabulary creation: Tokenization helps create a vocabulary, which is a collection of unique tokens in a corpus. The vocabulary is crucial for training language models and other NLP models. 

Data compression: By breaking the text into tokens, the data is compressed to some extent, making it more manageable for storage and analysis. 

Normalization: Tokenization also involves converting text to lowercase and handling other forms of normalization, which helps ensure consistent representation and reduces the vocabulary size. 

Stopword removal: Some tokenization processes remove common stopwords (e.g., "the," "is," "and") that often add little meaning to the text. 

Different tokenization approaches can be employed based on the specific requirements of the NLP task and the language being analyzed. For instance, languages with complex word structures like German may require specialized tokenization techniques compared to English, where words are generally separated by spaces. Moreover, tokenization is a crucial step in preparing text data for tasks such as text classification, named entity recognition, machine translation, sentiment analysis, and more.

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