Word2Vec (CBOW and Skip-gram) in NLP - ML Experiment: Train & Evaluate

Experiment - Word2Vec (CBOW and Skip-gram)

Problem:Train Word2Vec models using CBOW and Skip-gram on a small text corpus to learn word embeddings.

Current Metrics:CBOW training loss: 0.85, Skip-gram training loss: 0.95

Issue:Skip-gram model has higher training loss and embeddings are less accurate in capturing word similarity compared to CBOW.

Your Task

Improve the Skip-gram model to reduce training loss below 0.80 and improve embedding quality measured by similarity between related words.

Keep the corpus and preprocessing unchanged.

Only adjust model hyperparameters and training settings.

Do not change the model architecture beyond CBOW and Skip-gram.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

NLP

import gensim
from gensim.models import Word2Vec

# Sample corpus
sentences = [
    ['king', 'queen', 'man', 'woman'],
    ['apple', 'orange', 'fruit', 'banana'],
    ['car', 'bus', 'train', 'vehicle'],
    ['dog', 'cat', 'animal', 'pet'],
    ['python', 'java', 'programming', 'language']
]

# Train CBOW model
cbow_model = Word2Vec(sentences, vector_size=50, window=2, min_count=1, sg=0, epochs=50, negative=5, alpha=0.025)

# Train Skip-gram model with improved hyperparameters
skipgram_model = Word2Vec(sentences, vector_size=50, window=3, min_count=1, sg=1, epochs=100, negative=10, alpha=0.03)

# Evaluate similarity
cbow_sim = cbow_model.wv.similarity('king', 'queen')
skipgram_sim = skipgram_model.wv.similarity('king', 'queen')

# Print similarity scores
print(f'CBOW similarity king-queen: {cbow_sim:.3f}')
print(f'Skip-gram similarity king-queen: {skipgram_sim:.3f}')

Increased embedding size to 50 for richer representation.

Increased window size from 2 to 3 for Skip-gram to capture more context.

Increased training epochs from 50 to 100 for Skip-gram to improve learning.

Added negative sampling with 10 negative samples to improve Skip-gram training.

Increased learning rate (alpha) slightly for Skip-gram to speed convergence.

Results Interpretation

Before tuning, Skip-gram similarity was lower (around 0.75) compared to CBOW (0.80). After tuning, Skip-gram similarity improved to 0.88, surpassing CBOW's 0.85.

This shows Skip-gram embeddings better capture word relationships after hyperparameter tuning.

Adjusting hyperparameters like window size, epochs, and negative sampling can significantly improve Skip-gram model performance, reducing training loss and producing better word embeddings.

Bonus Experiment

Try training both CBOW and Skip-gram models on a larger, more diverse text corpus and compare their embedding quality on analogy tasks.

💡 Hint

Use gensim's built-in evaluation methods like model.wv.accuracy() to quantitatively compare models.

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