The Role of Word Embeddings in NLP: Word2Vec, GloVe, and FastText

What Are Word Embeddings?

In Natural Language Processing (NLP), word embeddings are a type of word representation that allows words to be represented as vectors in a continuous vector space. These embeddings capture the semantic and syntactic meaning of words by placing similar words close to each other in that space.

Before word embeddings, traditional methods like one-hot encoding or bag-of-words were used, but they suffered from major limitations:

They ignored word order and context.

They created sparse, high-dimensional vectors.

They failed to capture semantic similarity (e.g., “king” and “queen” would be unrelated).

Word embeddings solved these problems by learning dense, low-dimensional, and meaningful vector representations.

Why Are Word Embeddings Important in NLP?

Word embeddings are essential in modern NLP because they:

Capture semantic meaning (e.g., "Paris" is to "France" as "Berlin" is to "Germany").

Enable transfer learning (pre-trained embeddings can be used for multiple tasks).

Improve performance in downstream NLP tasks such as sentiment analysis, machine translation, named entity recognition, and more.

Key Word Embedding Models

1. Word2Vec (by Google)

Word2Vec is a popular model introduced by Google in 2013. It uses a shallow neural network to learn word embeddings in two main architectures:

CBOW (Continuous Bag of Words): Predicts a word given its context.

Skip-Gram: Predicts context words given a target word.

Key Features:

Trained using local context within a sliding window.

Efficient and scalable.

Captures linear relationships (e.g., king - man + woman ≈ queen).

Limitation:

Doesn't handle out-of-vocabulary words.

Each word gets a single vector, regardless of context.

2. GloVe (Global Vectors for Word Representation, by Stanford)

GloVe is a model that combines the advantages of global matrix factorization (like LSA) and local context window methods (like Word2Vec).

How it works:

Builds a co-occurrence matrix of words.

Learns embeddings by factoring this matrix in a way that preserves the ratios of co-occurrence probabilities.

Key Features:

Captures both local and global statistical information.

Results in word vectors with meaningful substructure.

Limitation:

Also gives a single vector per word.

Doesn’t consider subword information or handle out-of-vocabulary words well.

3. FastText (by Facebook)

FastText is an extension of Word2Vec that takes into account the internal structure of words.

How it works:

Treats each word as a bag of character n-grams.

Learns embeddings for these subword units and combines them to form word vectors.

Key Features:

Handles out-of-vocabulary words gracefully by using subword information.

Captures morphological information (useful for morphologically rich languages).

Advantage over Word2Vec and GloVe:

Better generalization to rare and unseen words.

Can generate embeddings for misspelled or novel words.

Summary Comparison

Feature Word2Vec GloVe FastText

Uses local context ✅ ✅ ✅

Uses global statistics ❌ ✅ ✅ (to some extent)

Handles OOV words ❌ ❌ ✅

Uses subword info ❌ ❌ ✅

Embedding per word Single Single Composed from n-grams

Conclusion

Word embeddings like Word2Vec, GloVe, and FastText revolutionized NLP by providing a way to represent words in a way that captures their meanings and relationships. Each method has its strengths and trade-offs, and the choice of which to use depends on the task, language, and the availability of training data.

While newer models like BERT and GPT use contextual embeddings, these earlier models still serve as the foundation for understanding how machines learn language representations.

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