In NLP, what does “Tokenization” mean?

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In the field of Natural Language Processing (NLP), tokenization is a regular activity. Both classic NLP techniques, such as Count Vectorizer, and cutting-edge Deep Learning-based structures, such as Transformers, rely on this phase extensively.


Tokenization is the process of breaking down a string of text into smaller chunks. Tokens in this context may be whole words, single letters, or parts of words. Therefore, tokenization may be broken down into three distinct categories: tokenizing words, tokenizing characters, and tokenizing subwords (n-gram characters).

Take the adage “Never lose hope” as an illustration.

Tokens are often formed using a space-based system. Tokenizing this text using space as a delimiter yields the following three tokens: Never-lose-hope. Word tokenization is illustrated by the fact that each token is itself a word.

Tokens, too, can be either single characters or parts of words. Take the word “smarter” as an illustration:

Smarter is a character token.

Acronyms: smart-er

Since tokens are the fundamental units of Natural Language, most text processing occurs at the token level.


State-of-the-art (SOTA) Deep Learning architectures in natural language processing, such as Transformer based models, operate on raw text at the token level. The token level is also used by the most common deep learning architectures for NLP, such as RNN, GRU, and LSTM.

Therefore, Tokenization is the first stage in the text data modeling process. The process of tokenizing the corpus yields the tokens. Then, a vocabulary is constructed using the tokens listed below. The collection of distinct items inside the corpus is known as its vocabulary. Keep in mind that there are two ways to build a vocabulary: either by examining each individual token in the corpus, or by focusing on the top K most common terms.

Let’s have a look at how this language is employed in both classic and cutting-edge Deep Learning-based NLP techniques.

The vocabulary is used as features in conventional NLP methods like Count Vectorizer and TF-IDF. Each word in the lexicon is seen as an individual quality:

Vocabulary is utilized to produce the tokenized input phrases in Advanced Deep Learning-based NLP systems. The last step is to feed the model the sentences’ tokens.


When it comes to complex NLP tasks like sentiment analysis, language translation, and topic extraction, tokenization is a crucial first step. Tokenization involves dividing a piece of text into smaller chunks, or tokens, such as individual words or phrases. Tokenization not only streamlines the rest of the NLP pipeline but also gives the model the ability to comprehend the meaning and context of individual words.

Tokenization may look simple, but it can really manage subtleties in language and adapt to various text structures. The quality of tokenization has a direct effect on how well an NLP system performs as a whole, hence it is of paramount significance in the field. More advanced tokenization approaches are anticipated to emerge as AI and machine learning continue to improve, improving NLP system performance even more.

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