Exploring Tokenization and Embeddings in Natural Language Processing

In the world of Natural Language Processing (NLP), tokenization and embeddings play critical roles in building effective and efficient models.

With the exponential growth of data, NLP techniques are employed across various industries, including healthcare, finance, customer service, and many others.

In this article, we will explore the key concepts of tokenization and embeddings, their significance in NLP, and how they contribute to creating powerful language models.

Tokenization: The First Step in NLP

Tokenization is the process of breaking down text into smaller units, called tokens. These tokens usually represent words, phrases, or sentences.

Tokenization is crucial for NLP because it helps convert unstructured text data into a structured format that can be easily understood and processed by machine learning algorithms.

There are two primary types of tokenization:

Word Tokenization

This approach splits a given text into individual words based on whitespace and punctuation marks. It is the most common form of tokenization and is suitable for languages with clear word boundaries, such as English.

Sentence Tokenization

This method divides the text into sentences. It uses punctuation marks, such as periods, exclamation points, and question marks, to identify sentence boundaries.

Sentence tokenization is particularly useful when the focus is on understanding the context or sentiment of a text.


Embeddings are a crucial step in NLP, as they transform tokens into numerical representations that can be easily processed by machine learning algorithms.

By converting words or phrases into vectors, embeddings capture semantic meaning and relationships between words in a high-dimensional space.

There are several popular embedding techniques used in NLP:

One-Hot Encoding

The simplest form of embedding, one-hot encoding represents each word as a binary vector with the length equal to the vocabulary size. A one is placed at the index corresponding to the word in the vocabulary, while all other positions are set to zero. Although straightforward, one-hot encoding can be inefficient due to its high dimensionality and the inability to capture semantic relationships between words.


Developed by Google, Word2Vec is a popular embedding technique that addresses the limitations of one-hot encoding. It generates dense word vectors in continuous space, capturing semantic relationships between words. Word2Vec uses two main architectures: Continuous Bag-of-Words (CBOW) and Skip-Gram. The former predicts a target word based on its surrounding context, while the latter predicts context words given a target word.

GloVe (Global Vectors for Word Representation)

Developed by Stanford University, GloVe is another widely-used embedding technique. It combines the benefits of both global matrix factorization methods and local context window approaches. By leveraging co-occurrence statistics, GloVe efficiently captures both semantic and syntactic information in word vectors.


FastText is an open-source, free, and lightweight library developed by Facebook’s AI Research (FAIR) team, designed for efficient learning of word representations and sentence classification tasks. It is an extension of the Word2Vec model and addresses some of its limitations, particularly when it comes to representing rare and out-of-vocabulary (OOV) words.

The primary difference between FastText and Word2Vec lies in their treatment of words. While Word2Vec generates embeddings for whole words, FastText operates on sub-word units, specifically n-grams of characters within each word. For example, if we choose n=3 for the word “apple,” the character n-grams would be [“<ap”, “app”, “ppl”, “ple”, “le>”], where ‘<‘ and ‘>’ denote the beginning and end of the word, respectively. By breaking words into smaller units, FastText is capable of capturing morphological information, which helps in generating more accurate word representations for morphologically rich languages.

The FastText model combines these character n-grams to create word embeddings, enabling it to represent rare and OOV words effectively. This approach also allows the model to understand word variations, such as different word forms or misspellings, that share similar character n-grams.

FastText is particularly useful in scenarios where the dataset contains a large number of rare words or when the model is required to handle multiple languages. The library provides pre-trained word vectors for various languages and supports a wide range of NLP tasks, including text classification, sentiment analysis, and information retrieval.

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