What is Tokenization in Natural Language Processing?

Tokenization is the first step to training natural language processing (NLP) models. It involves breaking down unstructured textual data into small units, known as tokens, that machine learning models can understand.

What is NLP?

NLP is a field of computer science and linguistics that enables machines to understand, interpret and respond to human language. 

From search engines, voice assistants, and chatbots to automated translation and sentiment analysis, NLP powers many of the technologies we use daily. However, computers don't process language like humans do; they require structured and numeric input to understand raw textual data.

This is where tokenization comes in. Tokenization is breaking down text into smaller, manageable units called tokens. 

It is the first step in any custom NLP project and plays a foundational role in helping machines interpret language. 

Despite its simplicity, tokenization profoundly impacts the performance and accuracy of language models, making it one of the most crucial stages in natural language processing projects.

Tokens might be words, parts of words (called subwords), or even individual characters depending on the specific requirements of the task at hand. 

The primary goal is to convert unstructured text into a structured format that machines can more easily process.

For example, the sentence “Tokenization helps machines understand language” might be split into the tokens:

["Tokenization", "helps", "machines", "understand", "language", "."]

This might seem like a simple task. But behind the scenes, tokenization involves handling punctuation, dealing with contractions (like “don’t” or “she’s”), and recognizing language-specific patterns. 

Think of tokenization as slicing up a loaf of bread, where each slice (or token) can then be individually analysed, fed into a model, or used to build more complex language structures. It is the first and most essential step in transforming text into data.

Types of Tokenization:

Tokenization can be applied at different levels of detail depending on the NLP task and the structure of the language being processed. 

The most common types include word, subword, character and sentence tokenization. Each type serves a unique purpose coming with its own set of strengths and challenges.

Word Tokenization

Word tokenization is the most intuitive and widely used form of tokenisation. Here, a sentence is split into individual words and punctuation marks. It works well for languages that use spaces to separate words.

“She’s learning NLP.”

→ ["She", "’", "s", "learning", "NLP", "."] (depending on the tokenizer)

While seemingly simple, this method faces challenges:

• Contractions like “can’t” may be split as ["ca", "n't"] or left intact.
• Punctuation needs to be separated or preserved meaningfully.
• Hyphenated words, such as “state-of-the-art,” must be handled carefully to preserve meaning.

Despite its limitations, word tokenization is often used in classic machine learning NLP tasks like text classification or topic modelling.

Subword Tokenization

Subword tokenization breaks words into smaller, meaningful units called subwords or word pieces. This is particularly useful for handling:

• Rare or unknown words that weren’t in the model’s original vocabulary
• Languages with compound words, like German or Finnish
• Misspellings or new word formations

“unbelievable” → ["un", "believ", "able"]

Subword tokenization strikes a balance between reducing vocabulary size and ensuring coverage of novel or complex words - making it essential for deep learning models.

Character Tokenization

In this approach, each character is treated as a separate token.

“hello” → ["h", "e", "l", "l", "o"]

While simple, it’s effective in certain scenarios:

• Languages without word delimiters
• Spelling corrections
• Low-resource datasets where overfitting is a concern

As character-level tokenization produces longer input sequences, it often requires more computational resources and takes longer to train models effectively.

 Sentence Tokenization

Sometimes known as sentence segmentation, this method splits large blocks of text into individual sentences.

“Dr. Lee went to the U.S. She arrived at 6 p.m.”

→ ["Dr. Lee went to the U.S.", "She arrived at 6 p.m."]

This step is especially important in tasks like:

• Text summarization
• Document classification
• Machine translation

Sentence tokenization must accurately distinguish between full stops that indicate the end of a sentence and those that serve other purposes, such as in; titles (like in Dr. or Mr.), abbreviations (e.g., etc.), or decimal numbers.

The Challenges of Tokenisation

While tokenization might appear to be a straightforward process, it presents several challenges - especially when working across multiple languages, domains or informal text formats. One of the most common issues is handling ambiguity.

For example, contractions like “don’t” may be split differently depending on the tokenizer, and words like “lead” can have multiple meanings depending on context.

Punctuation also complicates tokenization. Symbols like commas, full stops or quotation marks might belong with a word or stand alone and misinterpreting them can distort meaning. Abbreviations can be mistakenly identified as the end of a sentence in sentence tokenisation leading to inaccurate splits.

Multilingual tokenization adds another layer of complexity. Languages such as Chinese, Thai or Japanese do not use spaces between words, thus making it difficult to define token boundaries without additional linguistic knowledge or trained models.

Additionally, informal text, such as tweets, messages or social media posts can be complex as these often include emojis, hashtags and inconsistent grammar. These can confuse traditional tokenizers not designed to handle non-standard input.

Poor tokenization can weaken the performance of downstream tasks like parsing, classification or translation. This is why choosing or customizing the right tokenization strategy is a crucial step in any NLP workflow.

The Future of Tokenisation

As NLP technology continues to evolve, so does the approach to tokenization. Traditional word and subword tokenization remains vital, but newer models are beginning to move towards more flexible, end-to-end approaches that reduce dependence on predefined token boundaries. 

Emerging architectures are experimenting with byte-level and character-level inputs, allowing models to process raw text more directly and potentially manage multilingual and difficult data with greater ease.

This shift opens doors to language-agnostic models, capable of understanding a broader range of linguistic structures without custom tokenizers for each language. 

At the same time, tokenization tools are becoming smarter and better at adapting to informal text, emojis and other domain-specific text.

Although we may one day see tokenization integrated invisibly within robust, self-learning models, it remains a critical component of NLP today. 

The future lies in balancing efficiency, accuracy and adaptability as we continue refining how machines interpret language.

Why Tokenisation is Key

Tokenization is a fundamental yet often underappreciated step in Natural Language Processing. By breaking down complex, unstructured text into smaller and more meaningful units, tokenization enables machines to interpret and analyse language effectively. 

Whether it’s at the level of words, subwords, characters or sentences - the way text is tokenised has a direct impact on the performance of NLP models and applications.

Here at NetGeist, we offer the complete end-to-end service for developing NLP solutions like text-to-speech and speech-to-text technologies, including the initial tokenization tasks.  We create tools that enable you to tackle textual challenges through automation, processing and summarization.

No project is too big - our goal is to develop customized NLP solutions that would fit the concept of your company. Contact us to discuss your project.