Word Embeddings in NLP – Word2Vec & GloVe Tutorial for Beginners

Learn how word embeddings represent words as vectors in NLP. This beginner-friendly tutorial covers Word2Vec and GloVe, including Python examples to capture semantic relationships between words for AI and machine learning applications.

1. Introduction

Word embeddings are dense vector representations of words that capture semantic meaning and relationships.

  1. Unlike one-hot encoding, embeddings capture similarity between words.
  2. Essential for NLP tasks like text classification, sentiment analysis, and language modeling.

2. Word2Vec

Concept

  1. Developed by Google.
  2. Converts words into vectors based on surrounding context (Skip-gram or CBOW models).
  3. Captures relationships like: king - man + woman ≈ queen.

Python Example (Gensim Word2Vec)


from gensim.models import Word2Vec

# Sample sentences
sentences = [["I", "love", "AI"], ["AI", "is", "fun"], ["I", "love", "machine", "learning"]]

# Train Word2Vec model
model = Word2Vec(sentences, vector_size=50, window=3, min_count=1, workers=1)

# Word vector for 'AI'
vector = model.wv['AI']
print("Vector for 'AI':", vector)

# Similar words
similar = model.wv.most_similar('AI')
print("Similar words to 'AI':", similar)

3. GloVe (Global Vectors)

Concept

  1. Developed by Stanford.
  2. Captures global co-occurrence statistics of words in a corpus.
  3. Produces vectors where similar words have similar embeddings.

Python Example (Using Gensim for GloVe)


from gensim.scripts.glove2word2vec import glove2word2vec
from gensim.models import KeyedVectors

# Convert GloVe to Word2Vec format (example if you have glove.6B.50d.txt)
glove_input_file = 'glove.6B.50d.txt'
word2vec_output_file = 'glove.6B.50d.word2vec.txt'
glove2word2vec(glove_input_file, word2vec_output_file)

# Load embeddings
model = KeyedVectors.load_word2vec_format(word2vec_output_file, binary=False)

# Vector for 'AI'
vector = model['AI']
print("Vector for 'AI':", vector)

4. Best Practices

  1. Pretrained embeddings like Word2Vec or GloVe can save time and improve performance.
  2. Fine-tune embeddings on your dataset for task-specific accuracy.
  3. Normalize vectors for better similarity computations.
  4. Visualize embeddings using t-SNE or PCA for insights.

5. Outcome

After learning word embeddings, beginners will be able to:

  1. Represent words as dense vectors capturing semantic meaning.
  2. Use Word2Vec and GloVe embeddings for NLP tasks.
  3. Improve NLP model performance by leveraging semantic relationships between words.