What is Word2Vec?

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Word2Vec is a popular word embedding technique developed by Google (Mikolov et al., 2013) that represents words as dense vectors in such a way that words with similar meanings are placed close together in vector space.

🔹 How Word2Vec Works

Instead of treating words as discrete symbols (like one-hot encoding), Word2Vec learns word embeddings by predicting word-context relationships from large text corpora. It has two main models:

1. CBOW (Continuous Bag of Words)

   • Predicts the current word based on the context (surrounding words).

   • Example: Given "I ___ football", predict "play".

2. Skip-gram

   • Predicts the context words given the current word.

   • Example: Given "play", predict words like "football", "game", "team".

Both methods train a shallow neural network, and the hidden layer weights become the word embeddings.

🔹 Key Properties

• Captures semantic similarity:

  • "king" is close to "queen", "man" to "woman".

• Captures linear relationships:

  • king – man + woman ≈ queen

• Produces dense, low-dimensional vectors (e.g., 100–300 dimensions).

🔹 Advantages

• Efficient to train on large corpora.

• Produces embeddings that generalize well for NLP tasks.

• Handles semantic relationships better than one-hot or TF-IDF.

🔹 Limitations

• Produces static embeddings (same vector for "bank" in "river bank" and "savings bank").

• Context-independent → newer models like BERT or GPT embeddings are more powerful.

✅ In short:
Word2Vec is a neural network–based method that learns word embeddings by predicting words from their context. It was a breakthrough in NLP because it captured semantic meaning and relationships between words in a simple, efficient way.

Read more:

What are word embeddings?

What is lemmatization?

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