Word Embeddings: Word2Vec, GloVe, and FastText
Interview Preparation Hub for AI/ML Engineering Roles
1. Introduction
Word embeddings are dense vector representations of words that capture semantic meaning. Unlike traditional one-hot encoding, embeddings map words into continuous vector spaces where similar words are close together. They revolutionized Natural Language Processing (NLP) by enabling models to understand context, similarity, and relationships between words.
This guide explores three major embedding techniques—Word2Vec, GloVe, and FastText—covering fundamentals, mathematical foundations, architectures, training, applications, challenges, and interview notes.
2. Fundamentals of Word Embeddings
Word embeddings aim to capture distributional semantics: "You shall know a word by the company it keeps." They are trained on large corpora to learn word relationships.
- Dense Vectors: Low-dimensional, continuous representations.
- Semantic Similarity: Similar words have similar vectors.
- Contextual Meaning: Embeddings capture usage patterns.
3. Word2Vec
Word2Vec, introduced by Mikolov et al. in 2013, uses shallow neural networks to learn word embeddings. Two architectures are used:
- Continuous Bag of Words (CBOW): Predicts target word from context.
- Skip-Gram: Predicts context words from target word.
Skip-Gram Objective:
maximize Σ log P(context | word)
Word2Vec captures semantic relationships such as:
vector("King") - vector("Man") + vector("Woman") ≈ vector("Queen")
4. GloVe (Global Vectors)
GloVe, introduced by Pennington et al. in 2014, uses matrix factorization of co-occurrence statistics. It combines global corpus statistics with local context.
Objective:
Σ f(P_ij) (w_i^T w_j + b_i + b_j - log(P_ij))^2
GloVe embeddings capture semantic meaning by leveraging word co-occurrence probabilities across the entire corpus.
5. FastText
FastText, introduced by Facebook AI Research in 2016, extends Word2Vec by representing words as bags of character n-grams. This allows embeddings for rare and out-of-vocabulary words.
Word Representation:
vector(word) = Σ vector(n-grams)
FastText is particularly useful for morphologically rich languages.
6. Comparative Analysis
| Aspect | Word2Vec | GloVe | FastText |
|---|---|---|---|
| Approach | Predictive (neural network) | Count-based (matrix factorization) | Predictive + subword info |
| Strengths | Captures semantic relationships | Leverages global statistics | Handles rare words |
| Limitations | Struggles with rare words | Requires large corpus | Higher computational cost |
7. Applications
- Text Classification: Sentiment analysis, spam detection.
- Machine Translation: Mapping words across languages.
- Information Retrieval: Semantic search engines.
- Recommendation Systems: Content-based recommendations.
- Healthcare: Mining medical text for insights.
8. Challenges
- Bias in embeddings reflecting training data.
- Handling polysemy (words with multiple meanings).
- Need for large corpora.
- Static embeddings fail to capture dynamic context.
9. Interview Notes
- Be ready to explain Word2Vec CBOW and Skip-Gram.
- Discuss GloVe’s co-occurrence matrix factorization.
- Explain FastText’s use of subword information.
- Describe applications in NLP tasks.
- Know challenges like bias and polysemy.
Word2Vec → GloVe → FastText → Comparison → Applications → Challenges → Interview Prep
10. Final Mastery Summary
Word embeddings are foundational to modern NLP. Word2Vec introduced predictive embeddings, GloVe leveraged global co-occurrence statistics, and FastText extended embeddings with subword information. Together, they enabled models to understand semantic relationships and improved performance across tasks.
For interviews, emphasize your ability to explain these techniques clearly, discuss their mathematical foundations, and connect them to real-world applications. This demonstrates readiness for AI/ML engineering and research roles.