What is word embedding in NLP?

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In Natural Language Processing (NLP), a word embedding is a technique for representing words as dense numerical vectors in a continuous vector space, where words with similar meanings are located close to each other. Unlike simple one-hot encoding, which assigns sparse, high-dimensional vectors, embeddings capture semantic and syntactic relationships between words.

Why Word Embeddings?

• One-hot encoding → high-dimensional, no notion of similarity (e.g., "king" and "queen" are just orthogonal).

• Word embeddings → low-dimensional (50–300 dimensions typically), where similar words have closer vector representations.

How They Work

Word embeddings are learned from large text corpora using neural networks or statistical models. The idea: “You shall know a word by the company it keeps.” If two words often appear in similar contexts, their embeddings will be similar.

Popular Techniques

1. Word2Vec (CBOW & Skip-gram)

   • Learns word embeddings by predicting a word from its context (CBOW) or predicting context words from a target word (Skip-gram).

2. GloVe (Global Vectors)

   • Uses co-occurrence statistics across the corpus to learn embeddings.

3. FastText

   • Extends Word2Vec by considering subword information (character n-grams), helping with rare or unseen words.

Example

If trained well:

• vector("king") - vector("man") + vector("woman") ≈ vector("queen")
  This shows embeddings capture relationships like gender, tense, or semantic similarity.

Applications

• Text classification (spam detection, sentiment analysis).

• Machine translation.

• Named entity recognition.

• Question answering & chatbots.

• Semantic search and recommendation systems.

Summary

Word embeddings transform text into meaningful numerical representations where semantic relationships are preserved. They are a foundation of modern NLP, enabling machines to “understand” language beyond raw words.

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