Word2Vec (CBOW and Skip-gram) in NLP

Introduction

Word2Vec helps computers understand words by turning them into numbers based on their meaning. It learns which words appear together in sentences.

When you want to find similar words, like 'king' and 'queen'.

When you need to turn words into numbers for machine learning.

When you want to understand the meaning of words in a text.

When building chatbots that understand language better.

When grouping or clustering words by their meaning.

Syntax

NLP

from gensim.models import Word2Vec

model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, sg=0)

# sg=0 means CBOW, sg=1 means Skip-gram

sentences is a list of tokenized sentences (list of word lists).

vector_size sets the size of the word vectors (like how many numbers represent each word).

Examples

This creates a CBOW model with smaller vectors and a smaller window size.

NLP

model = Word2Vec(sentences, vector_size=50, window=3, sg=0)

This creates a Skip-gram model with bigger vectors and a bigger window size.

NLP

model = Word2Vec(sentences, vector_size=100, window=5, sg=1)

Sample Model

This code trains two Word2Vec models: one using CBOW and one using Skip-gram. It then shows the vector for the word 'machine' and finds words similar to 'machine' in both models.

NLP

from gensim.models import Word2Vec

# Sample sentences
sentences = [
    ['I', 'love', 'machine', 'learning'],
    ['Word2Vec', 'helps', 'understand', 'words'],
    ['Skip', 'gram', 'and', 'CBOW', 'are', 'models'],
    ['Machine', 'learning', 'is', 'fun']
]

# Train CBOW model (sg=0)
model_cbow = Word2Vec(sentences, vector_size=20, window=2, min_count=1, sg=0)

# Train Skip-gram model (sg=1)
model_sg = Word2Vec(sentences, vector_size=20, window=2, min_count=1, sg=1)

# Get vector for word 'machine'
vec_cbow = model_cbow.wv['machine']
vec_sg = model_sg.wv['machine']

# Find most similar words to 'machine' in CBOW
similar_cbow = model_cbow.wv.most_similar('machine')

# Find most similar words to 'machine' in Skip-gram
similar_sg = model_sg.wv.most_similar('machine')

print('CBOW vector for machine:', vec_cbow)
print('Skip-gram vector for machine:', vec_sg)
print('CBOW most similar to machine:', similar_cbow)
print('Skip-gram most similar to machine:', similar_sg)

Important Notes

CBOW predicts a word from its surrounding words, so it works well with frequent words.

Skip-gram predicts surrounding words from a given word, so it works better with rare words.

Word vectors are lists of numbers that capture word meaning based on context.

Summary

Word2Vec turns words into numbers that show their meaning.

CBOW and Skip-gram are two ways Word2Vec learns word meanings.

Use Word2Vec to find similar words or prepare text for machine learning.

Practice

1. What is the main difference between the CBOW and Skip-gram models in Word2Vec?

easy

A. CBOW uses one-hot encoding, Skip-gram uses frequency encoding.

B. CBOW predicts a word based on its context, while Skip-gram predicts context words from a target word.

C. CBOW is used only for sentences, Skip-gram only for paragraphs.

D. CBOW requires labeled data, Skip-gram does not.

Solution

1. Step 1: Understand CBOW model purpose

   CBOW tries to predict the target word using the surrounding context words.

2. Step 2: Understand Skip-gram model purpose

   Skip-gram tries to predict the surrounding context words given the target word.

3. Final Answer:

   CBOW predicts a word based on its context, while Skip-gram predicts context words from a target word. -> Option B

4. Quick Check:

   CBOW = context to word, Skip-gram = word to context [OK]

Hint: Remember CBOW = context to word, Skip-gram = word to context [OK]

Common Mistakes:

• Confusing which model predicts context vs. target word
• Thinking both models do the same prediction
• Assuming CBOW needs labeled data

2. Which of the following is the correct way to initialize a Skip-gram Word2Vec model using the Gensim library in Python?

easy

A. Word2Vec(sentences, size=100, window=5, sg=0)

B. Word2Vec(sentences, vector_size=100, window=5, sg=0)

C. Word2Vec(sentences, size=100, window=5, sg=1)

D. Word2Vec(sentences, vector_size=100, window=5, sg=1)

Solution

1. Step 1: Identify correct parameter for Skip-gram

   In Gensim, 'sg=1' sets Skip-gram, 'sg=0' sets CBOW.

2. Step 2: Use correct parameter names

   Since Gensim 4.0+, 'vector_size' replaces 'size' for embedding dimension.

3. Final Answer:

   Word2Vec(sentences, vector_size=100, window=5, sg=1) -> Option D

4. Quick Check:

   sg=1 and vector_size used correctly [OK]

Hint: Use sg=1 for Skip-gram and vector_size for embedding size [OK]

Common Mistakes:

• Using 'size' instead of 'vector_size' in recent Gensim versions
• Setting sg=0 which is CBOW, not Skip-gram
• Confusing sg parameter values

3. Given the following code snippet using Gensim's Word2Vec with Skip-gram, what will be the output of model.wv.most_similar('king', topn=1) if the model is trained on a typical English corpus?

medium

A. [('run', similarity_score)]

B. [('apple', similarity_score)]

C. [('queen', similarity_score)]

D. [('car', similarity_score)]

Solution

1. Step 1: Understand Word2Vec similarity

   Word2Vec finds words with similar meanings or contexts; 'queen' is semantically close to 'king'.

2. Step 2: Analyze typical English corpus relations

   Words like 'apple', 'car', or 'run' are unrelated to 'king' in meaning or context.

3. Final Answer:

   [('queen', similarity_score)] -> Option C

4. Quick Check:

   Most similar to 'king' is 'queen' [OK]

Hint: Most similar to 'king' is usually 'queen' in English corpora [OK]

Common Mistakes:

• Choosing unrelated words as most similar
• Confusing syntactic similarity with semantic similarity
• Expecting exact similarity scores

4. You trained a CBOW Word2Vec model but get an error: KeyError: 'unknown_word' when querying model.wv['unknown_word']. What is the most likely cause and fix?

medium

A. The word was not in training data; retrain with larger corpus or check vocabulary before querying.

B. The model was trained with Skip-gram; switch to CBOW to fix.

C. The vector size is too small; increase vector_size parameter.

D. The window size is too large; reduce window parameter.

Solution

1. Step 1: Understand KeyError cause

   KeyError occurs when the queried word is not in the model's vocabulary.

2. Step 2: Fix by ensuring word presence

   Either add the word to training data or check if word exists before querying to avoid error.

3. Final Answer:

   The word was not in training data; retrain with larger corpus or check vocabulary before querying. -> Option A

4. Quick Check:

   KeyError means word missing in vocabulary [OK]

Hint: Check if word is in vocabulary before querying model vectors [OK]

Common Mistakes:

• Assuming model type (CBOW/Skip-gram) causes KeyError
• Changing vector or window size to fix missing word error
• Ignoring vocabulary check before querying

5. You want to train a Word2Vec model to capture rare word meanings better. Which approach is best?

hard

A. Use Skip-gram with a smaller window size and increase training epochs.

B. Use CBOW with a large window size and fewer epochs.

C. Use Skip-gram with a large window size and fewer epochs.

D. Use CBOW with a smaller window size and increase training epochs.

Solution

1. Step 1: Identify model for rare words

   Skip-gram is better at learning rare word representations than CBOW.

2. Step 2: Adjust window size and epochs

   Smaller window focuses on close context, improving rare word meaning; more epochs improve training quality.

3. Final Answer:

   Use Skip-gram with a smaller window size and increase training epochs. -> Option A

4. Quick Check:

   Skip-gram + small window + more epochs = better rare word capture [OK]

Hint: Skip-gram + small window + more epochs helps rare words [OK]

Common Mistakes:

• Choosing CBOW for rare word learning
• Using large window size which dilutes context
• Reducing epochs which limits training