What are Word Embeddings?

Word embeddings are a fundamental concept in Natural Language Processing (NLP), enabling machines to understand and process human language effectively. They transform words into numerical representations, capturing semantic and syntactic relationships. This guide provides a comprehensive overview of word embeddings, progressing from basic to advanced concepts, and includes practical examples to enhance understanding.

1. Introduction to Word Embeddings

What Are Word Embeddings?

Word embeddings are a way to represent words as numbers (vectors) in a way that captures their meanings and relationships. Unlike traditional representations like one-hot encoding, which are sparse and high-dimensional, word embeddings capture the context, meaning, and relationships between words in a lower-dimensional space. This allows models to perform arithmetic operations to find word relationships, such as “king - man + woman = queen.”

Why Use Word Embeddings?

• Semantic Similarity: Words with similar meanings (like “cat” and “kitten”) have similar vectors.
• Efficiency: They use fewer dimensions than older methods, saving space and computation.
• Better Understanding: Models can learn relationships between words, improving tasks like translation or sentiment analysis.

2. Basic Concepts

2.1 One-Hot Encoding

Before delving into word embeddings, it’s essential to understand one-hot encoding:

• Definition: A representation where each word is encoded as a vector the size of the vocabulary, with all elements set to 0 except for the index corresponding to the word, which is set to 1.

Example:

For a vocabulary of [“apple”, “banana”, “cherry”]:

Limitations:

• High Dimensionality: The vector length equals the vocabulary size, leading to inefficiency with large vocabularies.
• Lack of Semantic Information: (no meaningful relationships) Does not capture relationships between words; all are equidistant.

2.2 Distributed Representation

Word embeddings address the limitations of one-hot encoding by providing distributed representations:

• Dense Vectors: Words are represented in continuous vector spaces with fewer dimensions.
• Semantic Proximity: Similar words have similar vectors, capturing meanings and relationships.

Example:

In a 3-dimensional embedding space:

• “king” → [0.25, 0.80, 0.45]
• “queen” → [0.30, 0.85, 0.50]
• “apple” → [0.60, 0.10, 0.70]
• “banana” → [0.55, 0.15, 0.75]

Here, “king” and “queen” have similar vectors, reflecting their related meanings.

3. Intermediate Concepts

3.1 Word2Vec

Word2Vec is a seminal model (foundational or highly influential model within a particular field of study) that generates word embeddings using neural networks. It comes in two architectures:

• Continuous Bag of Words (CBOW): Predicts a target word based on its context (surrounding words).
• Skip-Gram: Predicts the context words given a target word.

Example:

For the sentence “The cat sits on the mat”:

• CBOW: Input: [“The”, “cat”, “on”, “the”, “mat”], Output: “sits”
• Skip-Gram: Input: “sits”, Output: [“The”, “cat”, “on”, “the”, “mat”]

Word2Vec captures semantic relationships, enabling vector operations like:

king - man + woman ≈ queen

This demonstrates that the vector difference between “king” and “man” is similar to that between “queen” and “woman,” capturing gender relationships.

3.2 GloVe

GloVe (Global Vectors for Word Representation) builds embeddings by analyzing how often words appear together in a large text corpus (co-occurrence). Words that frequently appear in similar contexts will have similar vectors.

Example:

Words like “ice” and “water” appear in similar contexts, so their vectors are positioned closely in the embedding space, reflecting their related meanings.

3.3 FastText

FastText improves on Word2Vec by breaking words into smaller parts (subwords). This helps it handle rare or unseen words better.

Example:

The word “unhappiness” might be represented by the character n-grams: “un”, “hap”, “piness”, etc. This allows the model to understand related words like “happy,” “unhappy,” and “happiness” through shared subword information.

4. Advanced Concepts

4.1 Contextual Word Embeddings: Adapting to Sentences

Traditional embeddings assign one fixed vector per word regardless of context. However, contextual embeddings generate different vectors based on how the word is used in a sentence.

Example: The word “bank” has different meanings:

1. “He sat by the river bank.” (riverbank)
2. “She deposited money in the bank.” (financial institution)

Contextual models like BERT (Bidirectional Encoder Representations from Transformers) create different embeddings for each case based on surrounding words.

5. Practical Applications of Word Embeddings

5.1 Sentiment Analysis

Embeddings help classify whether text is positive or negative by capturing the meaning of words like “happy” or “sad.”

5.2 Semantic Search

Search engines use embeddings to match queries with relevant documents based on meaning rather than exact keywords.

5.3 Machine Translation

Embeddings align words from different languages into the same vector space so that equivalent terms (e.g., “dog” in English and “perro” in Spanish) have similar vectors.

Conclusion

Word embeddings are essential for modern NLP tasks because they allow machines to understand relationships between words efficiently and meaningfully. From simple models like Word2Vec to advanced techniques like BERT, they continue to evolve and improve our ability to process language computationally.