Understanding The Importance Of Tokenization In Machine Learning


In this article, we will discuss the concept of Tokenization and understand its purpose and applications. We will also discuss different tokenization approaches with their pros and cons and why we need Tokenization.

What is Artificial Intelligence(AI)?

Artificial Intelligence, often known as AI, refers to the technology of developing computers and robots that are capable of behaving in a way that they can mimic human capabilities. This involves creating machines that can imitate human intelligence.AI systems can be broadly classified into two categories- Weak and Strong.

  1. Weak AI, also known as narrow AI, encompasses systems designed to excel in a specific task. From video games that adapt to players' strategies to virtual assistants like Amazon's Alexa and Apple's Siri that interpret and respond to vocal commands, these AI systems demonstrate their aptitude in a particular field.
  2. On the other hand, Strong AI systems display cognitive capabilities akin to human beings. These systems are more intricate, and their responsibilities extend to tasks that typically require human ingenuity and problem-solving skills. For instance, self-driving cars navigating through city traffic or robotic systems assisting in hospital operating rooms reflect the manifestation of strong AI. They are designed to operate autonomously and handle situations that call for solutions without any human intervention.

What is Machine Learning(ML)?

Machine Learning is a specialized branch nested within the larger realm of Artificial Intelligence. It's centered around the principle of training machines to accomplish specified tasks accurately by discerning patterns and making informed predictions. In simpler words, AI is computer software that mimics the ways that humans think to perform complex tasks, such as analyzing, reasoning, and learning. Meanwhile, ML is a subset of AI that uses algorithms trained on data to produce models that can perform complex tasks. Through continuous learning from vast volumes of data, these models adapt and refine their ability to perform, paving the way for increasingly accurate and reliable outcomes.

What is NLP?

Natural Language Processing (NLP) is a branch of computer science and artificial Intelligence focused on enabling computers to understand, interpret, and generate human language. NLP means teaching a computer to recognize patterns in human speech and use those patterns to analyze and respond to speech input, much like humans process speech. For more information, Read the article What Is Natural Language Processing (NLP)?

What is Tokenization in Machine Learning?

Tokenization is a fundamental process in natural language processing (NLP) that plays a crucial role in transforming raw text into a format suitable for further analysis and modeling. It involves breaking down a piece of text, such as a sentence or a document, into smaller units called tokens. These tokens can be individual words, subwords, or even characters, depending on the specific task and the level of granularity required. In simpler words, Tokenization is the process of breaking the text into words and sentences called tokens. These tokens help in understanding the context or developing the model for the NLP.

For example, the text 'Hello world' will be tokenized into 'Hello' 'world'.

Types of Tokenization in Machine Learning

Tokens can vary based on the tokenization technique used and the level of linguistic abstraction required for the task at hand.

  1. Word Tokenization: In word tokenization, the text is segmented into individual words. Each word in the sentence becomes a separate token. For example, Input Text "The quick brown fox jumps over the lazy dog." Tokens ["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog", "."]
  2. Sentence Tokenization: Sentence tokenization refers to the process of splitting a text into individual sentences. It is a common preprocessing step in natural language processing tasks where the input text needs to be divided into meaningful units at the sentence level. For example, Input Text "Unsupervised learning is fascinating. It is a part of Machine learning." Tokens ["Unsupervised learning is fascinating.", "It is a part of Machine learning."]
  3. Character Tokenization: In character tokenization, each character in the text becomes a token. This level of granularity is helpful for character-level language modeling or generating text. For example, Input Text "Hello, World!" Tokens ["H", "e", "l", "l", "o", ",", " ", "W", "o", "r", "l", "d", "!"]

Note. Editor tool Colab(Google Colaboratory, popularly known as Colab, is a web IDE for Python).

# !pip install nltk
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
text = "This is an example sentence. This is an sample text."
# Word Tokenization
tokens = word_tokenize(text)
print("Word Tokenization: ", tokens)
# Sentence Tokenization
sentences = sent_tokenize(text)
print("Sentence Tokenization:",sentences)

In the above code, the nltk library is installed in the editor. After that importing the functions which will be used i.e. word_tokenize() and sent_tokenize(). Where word_tokenize() is a function used to split the text into words, and the sent_tokenize() function is used to split the text into sentences.



Here, we have discussed some of the common techniques used for Tokenization with their examples.

1. Word Tokenization

Word tokenization is also known as word segmentation and is the process of splitting a text or sentence into individual words or tokens. In this approach, the text is segmented at the word level, where each word becomes a separate token. Word tokenization is a fundamental step in natural language processing (NLP) and text analysis, as it forms the foundation for various language-related tasks.


import nltk
from nltk.tokenize import word_tokenize
text = "This is an example sentence. This is an sample text."
# Word Tokenization
tokens = word_tokenize(text)
print("Word Tokenization: ", tokens)

In this code, the nltk library is imported. Then defining the text which we want to tokenize. After that word_tokenize() function is used to split the text into individual words.


Advantages of Word Tokenization

  1. Text Understanding: By breaking down text into words, we gain a granular understanding of the underlying content. The word-level analysis allows us to capture semantic meaning, syntactic structure, and contextual information present in the text.
  2. Feature Extraction: Tokens generated through word Tokenization serve as features for machine learning models. Each word becomes a separate feature that can be used to train models for various NLP tasks such as sentiment analysis, text classification, named entity recognition, and machine translation.
  3. Language Processing: Word tokenization forms the foundation for many language processing tasks, such as part-of-speech tagging, lemmatization, and syntactic parsing. These tasks rely on individual word units for accurate analysis and interpretation of text data.
  4. Statistical Analysis: By tokenizing text into words, it becomes possible to perform statistical analysis on the data. Word frequencies, co-occurrence patterns, and other statistical measures can be computed to gain insights into the text corpus and identify important patterns or topics.
  5. Text Preprocessing: Word tokenization is a crucial step in text preprocessing, allowing for further cleaning, normalization, and removal of stop words or irrelevant characters. It helps standardize the text data and improve subsequent NLP tasks' quality.

Disadvantages of Word Tokenization

  1. Ambiguity: Some words can have multiple meanings depending on the context. Word tokenization may not always capture the precise intended meaning. Contextual information and additional NLP techniques are often required to disambiguate such cases.
  2. Out-of-Vocabulary: Word tokenization may encounter words that are not present in the vocabulary or training data. Out-of-vocabulary (OOV) words can pose challenges for downstream tasks, as the model may struggle to handle them effectively.
  3. Tokenization Errors: Tokenization algorithms may not always accurately segment words, especially in the presence of unstructured text data. Errors can occur with compound words, hyphenated words, contractions, or special characters, leading to incorrect token boundaries.
  4. Language-Specific Challenges: Different languages present unique challenges for word tokenization. Languages with rich structures may require more advanced tokenization techniques to handle word boundaries effectively.

2. Sentence Tokenization

Sentence tokenization refers to the process of splitting a text into individual sentences. It is a common preprocessing step in natural language processing tasks where the input text needs to be divided into meaningful units at the sentence level.


import nltk
# Sample text
text = "Hello! How are you? I hope you're doing well. Have a great day!"
# Tokenize the text into sentences
sentences = nltk.sent_tokenize(text)
# Print the tokenized sentences
for sentence in sentences:

In the above code, the nltk.sent_tokenize() function is used to tokenize the text. Here the input text is provided, and then the function returns a list of sentences.


Advantages of Sentence Tokenization

  1. Text Understanding: Sentence tokenization helps in breaking down a text into meaningful and understandable units at the sentence level. This granularity allows for better comprehension and analysis of the text.
  2. Language Processing: Many natural language processing (NLP) tasks, such as sentiment analysis, text summarization, and machine translation, operate at the sentence level. Sentence tokenization provides the necessary input structure for these tasks.
  3. Context Preservation: By segmenting text into sentences, the context within each sentence is preserved. This is valuable when processing and analyzing text, as sentences often contain distinct information or convey specific meanings.
  4. Text-to-Speech Conversion: Sentence tokenization is useful in text-to-speech applications, where breaking the text into sentences helps generate more natural and fluent speech.

Disadvantages of Sentence Tokenization

  1. Ambiguity: Some sentences can be inherently ambiguous, making it challenging to accurately segment them. For example, sentences with abbreviations and acronyms used in non-sentence contexts may pose difficulties for sentence tokenizers.
  2. Language-specific Challenges: Different languages have unique sentence structures and punctuation conventions. Sentence tokenization algorithms may not perform equally well across all languages, requiring language-specific considerations and adjustments.
  3. Style and Formatting Variations: Text from various sources, such as social media, emails, or online forums, may have unconventional sentence boundaries or lack punctuation. This can pose challenges for sentence tokenization, as the absence of clear delimiters can make it harder to determine sentence boundaries accurately.
  4. Over-segmentation or Under-segmentation: Sentence tokenizers may occasionally split sentences incorrectly, leading to over-segmentation (where a sentence is divided into multiple smaller sentences) or under-segmentation (where multiple sentences are considered as a single sentence). These errors can impact downstream NLP tasks and require careful evaluation and adjustments.

3. Character Tokenization

Character tokenization is a technique that breaks down a piece of text into individual characters. Each character in the text becomes a separate token, enabling analysis and modeling at the character level.


import nltk
text = "This is an example."
tokens = list(text)

In the above code, we first import the nltk library. Then define the input text that we want to tokenize using character tokenization. To tokenize the text, we convert the text into a list using the list() function, which splits the text into individual characters and then stores the characters in tokens.


Advantages of Character Tokenization

  1. Fine-Grained Analysis: Character tokenization allows for a detailed analysis of individual characters, capturing spelling variations, textual noise, and character-based patterns that may be missed by higher-level tokenization techniques.
  2. Handling Rare Words: Character tokenization enables the handling of rare or unknown words, including out-of-vocabulary terms, by representing them at the character level. This is particularly useful in scenarios where the vocabulary is limited or when dealing with specialized domain-specific terms.
  3. Morphologically Rich Languages: Character tokenization is well-suited for languages with complex morphological structures, such as agglutinative or highly inflected languages. It enables capturing the intricate morphological information present in such languages, facilitating accurate analysis and modeling.
  4. Text Generation: Character tokenization is crucial for text generation tasks, where models generate text characters by character. By learning the patterns and relationships between characters, models can generate coherent and contextually relevant text.

Disadvantages of Sentence Tokenization

  1. Increased Dimensionality: This character tokenization can lead to computational challenges and increased model complexity, especially when dealing with large vocabularies or lengthy texts, as each character becomes a separate feature.
  2. Lack of Semantic Information: Character tokens lack the semantic meaning associated with higher-level tokens, such as words. This may limit the ability of models to capture semantic relationships and contextual information that exist at the word or phrase level.
  3. Longer Sequences: Character tokenization often results in longer input sequences compared to word-level Tokenization. This can lead to increased training and inference times, as well as potential challenges in modeling long-range dependencies.
  4. Limited Contextual Understanding: Character tokens may not capture larger contextual information present in the text. This can impact tasks that rely on understanding phrases, idioms, or specific word combinations, as the model may not have access to higher-level linguistic units.

Choosing the Right Tokenization Approach

The choice of tokenization approach depends on the specific NLP task and the characteristics of the text data. Word tokenization is commonly used for general-purpose tasks like sentiment analysis and text classification. Sentence tokenization is mainly used where a complete text needs to be divided at the sentence level to the meaningful and understandable. Character tokenization is suitable for character-level text generation and certain language modeling tasks.

Why do we Tokenize?

Tokenization is essential in NLP because it enables text preprocessing, feature generation, vocabulary creation, sequence representation, and model input preparation. It helps us unlock valuable insights from textual data and facilitates the application of machine learning techniques to solve various NLP tasks. And provides a structured representation of text data that can be used for various NLP tasks.

  • Text Processing: Tokenization breaks down text into smaller units, such as words or characters, making it easier to process and analyze.
  • Feature Generation: Tokens act as features for machine learning models, capturing important information from the text.
  • Vocabulary Creation: Tokenization helps build a dictionary of unique tokens, enabling models to work with text in a numeric format.
  • Sequence Representation: Tokens preserve the order of words, enabling models to understand the sequential nature of text data.
  • Model Input: Tokenization prepares text data as numerical input for machine learning models in NLP tasks.

Other Python's Standard Libraries For Tokenization


# !pip install spaCy
import spacy
nlp = spacy.load("en_core_web_sm")
text = "This is an example sentence. Tokenization is important for NLP tasks."
doc = nlp(text)
# Word Tokenization
tokens = [token.text for token in doc]
# Sentence Tokenization
sentences = [sent.text for sent in doc.sents]

In the above code, the spaCy library is installed. For word tokenization and sentence Tokenization, a list comprehension is used to extract the text.



# !pip install transformers
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
text = "This is an example sentence. Tokenization is important for NLP tasks."
# Word Tokenization using BERT tokenizer
tokens = tokenizer.tokenize(text)

In the above code, the transformers library is installed, and BertTokenizer class is imported from this class 'bert-base-uncased' model is loaded, and for word tokenization BertTokenizer is used with the tokenizer.tokenize() method.



Tokenization is a process used in natural language processing (NLP) to break down text into smaller units called tokens. These tokens can be words, parts of words, or even individual characters, depending on what we need to analyze. There are different types of Tokenization, such as word, subword, and character tokenization, and discuss their benefits and drawbacks. Word tokenization helps us understand the text, extract important features, and analyze the language. Subword tokenization is useful for handling complex words, while character tokenization allows us to analyze individual characters.


Q. Can we perform Tokenization in other programming languages also?

A. Tokenization can be performed in other programming languages as well. While the specific libraries and methods may differ, the general concept of breaking down text into smaller units remains the same.

Q. Which library is used for Tokenization?

A. Here are some popular Python libraries used for Tokenization in natural language processing (NLP).

  1. NLTK (Natural Language Toolkit): NLTK provides various tokenization methods, including word tokenization and sentence tokenization, through its tokenization module.
  2. spaCy: spaCy is a powerful and efficient library for NLP tasks. It includes a tokenizer that can handle the Tokenization of words, sentences.
  3. Transformers: Transformer is a state-of-the-art library for natural language processing, particularly for tasks like text classification and language generation. It includes tokenization capabilities tailored to transformer-based models like BERT, GPT, and RoBERTa.

Q. What are some common challenges associated with Tokenization?

A. One significant challenge that arises with word tokens is the handling of Out-Of-Vocabulary (OOV) words. OOV words are newly encountered words during testing that do not exist in the pre-existing vocabulary.

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