Tokenization Explained: How NLP Models Understand Text

Ever tried learning a new language from scratch? It can feel daunting with layers of unfamiliar words and grammar. You naturally start breaking sentences into words and recognizing repeating patterns like prefixes or suffixes. Computers face similar challenges. To comprehend human language, they first need to break it down into manageable pieces called tokens. This process, known as tokenization, is fundamental in Natural Language Processing (NLP).

In this guide, we’ll explore what tokenization is, its significance, various types (words, characters, subwords, and sentences), and briefly touch on popular tools.

What is Tokenization and Why Is It Important?

Tokenization is the process of dividing text into smaller units (tokens), such as words, characters, subword segments, or sentences. It's essential because computers don't naturally understand language; they interpret structured data. Tokenization transforms raw text into structured tokens that models can analyze and assign meaning to.

For example, take the sentence: "Oman is a bird watcher’s paradise." A computer sees this initially as a meaningless string of characters. Tokenization transforms it into understandable units:

["Oman", "is", "a", "bird", "watcher", "’s", "paradise", "."]

This structured format allows NLP models to interpret and analyze text efficiently. In more advanced models, each token, such as “Oman” or “paradise”,is also mapped to a numerical representation based on a learned vocabulary. These numbers help the model understand the meaning of the text. (We’ll explore this process more deeply later in the post.)

Types of Tokenization

Tokenization can happen at different levels depending on the task and the language model using the tokenization. Below, we use the same example sentence to show how tokenization works in four common levels: sentence, word, character, and subword.

Example sentence:
"Oman is a bird watcher’s paradise."

1. Sentence Tokenization: Splits the text into complete sentences.

Tokens:
["Oman is a bird watcher’s paradise."]

Advantages:

• Useful for tasks like summarization or translation where sentence structure matters.

Disadvantages:

• Can misidentify sentence boundaries when punctuation is ambiguous, such as periods in abbreviations.
• In some models, you cannot represent out-of-vocabulary tokens (sentences that weren’t present in the learning corpus).

1. Word TokenizationSplits each sentence into individual words and punctuation marks.

Tokens:
["Oman", "is", "a", "bird", "watcher", "’s", "paradise", "."]

Advantages:

• Easy to understand and widely used.
• Suitable for general text processing and building word-level vocabularies.

Disadvantages:

• Struggles with out-of-vocabulary or misspelled words. For example, “watching”, “watch”, and “watcher” are completely different tokens in this type of tokenization. And if the model during training didn’t see one of those words, it will consider them out-of-vocabulary and will not be able to recognize or deal with them.
• Language-dependent and assumes clear word boundaries.

1. Character Tokenization: Breaks the sentence into individual characters.

Tokens (partial):
['O', 'm', 'a', 'n', ' ', 'i', 's', ' ', 'a', ' ', 'b', 'i', 'r', 'd', ' ', 'w', 'a', 't', 'c', 'h', 'e', 'r', '’', 's', ' ', 'p', 'a', 'r', 'a', 'd', 'i', 's', 'e', '.']

Advantages:

• Handles any language and never fails on unknown words.
• Useful for languages with no spaces between words.

Disadvantages:

• Leads to long sequences, which could be computationally very expensive in some model architectures
• Requires models to learn patterns across many characters to capture meaning. In other words, the training phase of the model might take longer because it first needs to learn the connections between characters before it starts to learn the connections between words or phrases.

1. Subword Tokenization: Splits words into smaller known components. This is by far the most adapted type of tokenization nowadays as it comprises between two extremes: sentence tokenization and character tokenization

Tokens:
["Oman", "is", "a", "bird", "watch","##er", "’", "s", "para", "##dise", "."]

Advantages:

• Balances vocabulary size and flexibility. Here, it recognizes that some suffixes like “er” is common between words and therefore it has its own token.
• Efficient for handling rare or compound words.

Disadvantages:

• Slightly more complex to interpret.
• Requires pre-learned vocabularies or models like BERT or GPT.

Challenges and Considerations in Tokenization

Tokenization may seem simple. After all, just split on spaces and punctuation, right?Not really, real text throws many curve balls. Key issues include:

• Punctuation and abbreviations: Periods in “Dr.” or “LLC” can confuse sentence splitters and word tokenizers. 
• Contractions and clitics: “don’t” can be one token or split into “do” + “n’t” to help models learn negation.
• Out-of-vocabulary words: Rare terms and typos become <unk> in word models; subword methods reduce this but need a well-chosen vocab size. For example, “بركاء” might be recognized by a tokenizer. However, if written as “بركا”it might not be recognized at all. When we talk about tens of thousands of tokens, such decisions need to be made at a scale, which just complicates the process.
• Multilingual text: Languages without spaces (Chinese, Thai) need special segmenters. German compounds or Arabic affixes demand extra rules.
• Tashkeel and diacritics
  Arabic diacritic marks (fatha, damma, kasra, etc.) may be present or omitted.preserving them helps disambiguate homographs (e.g.عِلْم vs علم) but multiplies token variants; stripping them simplifies the token set but loses pronunciation and meaning cues.
• Special formats: Emails, URLs, hashtags, dates and code snippets require balanced splitting to preserve meaning.
• Model compatibility: Always use the tokenizer the model was trained with to avoid embedding mismatches.
• Efficiency vs precision: Character tokenization covers everything but yields long sequences. Word tokenization is faster but risks unknown tokens. Subword strikes a middle path.

Advanced Subword Algorithms

Subword methods handle rare words and control vocabulary size with minimal manual rules:

1. Byte Pair Encoding (BPE)
   Start from individual characters and iteratively merge the most frequent adjacent pairs until you reach a target vocabulary. You get a mix of full words, common prefixes and suffixes, and single letters. This is the most used type of tokenization nowadays
2. WordPiece
   Similar merging process but guided by a likelihood objective, choosing merges that maximize the probability of the training data. This is the core of BERT’s tokenizer, using longest-match segmentation at inference.
3. Unigram Language Model (SentencePiece)
   Begin with a large candidate set of substrings and prune low-probability tokens via an EM algorithm. Keeps all single characters so any word can be represented, and supports subword regularization during training.

Conclusion

Tokenization is far more than a simple split on spaces, it shapes every downstream step in your NLP workflow. Whether you choose sentences, words, characters or subwords, each strategy brings its own balance of granularity, speed and coverage. In languages like Arabic, handling tashkeel, affixes and local place names adds extra nuance, while subword algorithms such as BPE, WordPiece and Unigram let you tame rare terms without exploding your vocabulary.

By understanding these trade-offs and using the exact tokenizer your model expects, you lay the groundwork for accurate embeddings and reliable predictions. Start small, pick a sample of your own text, try out NLTK, spaCy or Hugging Face tokenizers, and inspect how different methods break it apart. When you’re ready, dive deeper: train a custom subword tokenizer on your domain data to squeeze out extra performance. Get tokenization right, and your models will thank you with clearer, more consistent results.