What are word embeddings?

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Word embeddings are a type of word representation in Natural Language Processing (NLP) where words are mapped into continuous vector space (usually dense, low-dimensional vectors).

🔹 Key Idea

• Instead of representing words as one-hot vectors (sparse, high-dimensional, and with no notion of meaning), embeddings map words into vectors where similar words have similar representations.

• These embeddings capture semantic and syntactic relationships between words.

🔹 Example

• One-hot encoding of "king" = [0,0,0,1,0,...] (huge and meaningless without context).

• Word embedding of "king" = [0.25, -0.87, 0.44, ...] (dense vector).

Relationships emerge:

• king – man + woman ≈ queen

• Paris – France + Italy ≈ Rome

🔹 Popular Word Embedding Techniques

1. Word2Vec (Google) – Uses Skip-gram / CBOW to learn embeddings.

2. GloVe (Stanford) – Learns embeddings from word co-occurrence statistics.

3. FastText (Facebook) – Embeds words based on character n-grams (handles rare words better).

4. Contextual embeddings (modern) – BERT, GPT → embeddings depend on context.

   • Example: "bank" in "river bank" vs. "savings bank" will have different embeddings.

🔹 Why Word Embeddings Matter

• Improve performance in tasks like sentiment analysis, machine translation, chatbots, search engines, and question answering.

• Help models understand semantic similarity, analogies, and relationships.

✅ In short:
Word embeddings are dense vector representations of words that capture their meaning and relationships in a mathematical space, making NLP models smarter and more context-aware.

Read more:

What is stemming?

What is lemmatization?

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