Word2Vec (CBOW and Skip-gram) in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main goal of Word2Vec?

Word2Vec aims to learn word meanings by turning words into numbers (vectors) so that words with similar meanings have similar vectors.

beginner

Explain the Continuous Bag of Words (CBOW) model in Word2Vec.

CBOW predicts a target word based on its surrounding words (context). It looks at the words around a missing word and guesses what the missing word is.

beginner

What does the Skip-gram model do in Word2Vec?

Skip-gram takes a target word and tries to predict the words around it (context). It learns which words tend to appear near the target word.

beginner

Why is Word2Vec useful in real life?

Word2Vec helps computers understand language better, which improves things like search engines, chatbots, and translation by knowing word meanings and relationships.

intermediate

How do CBOW and Skip-gram differ in training focus?

CBOW predicts the center word from context words, while Skip-gram predicts context words from the center word. CBOW is faster; Skip-gram works better with rare words.

What does the Skip-gram model predict?

A Surrounding words from the target word

B The target word from surrounding words

C The next sentence in a paragraph

D The frequency of a word in a document

Skip-gram predicts the context words around a given target word.

Which Word2Vec model is generally faster to train?

A Neither, they are slow

B Skip-gram

C Both are equally fast

D CBOW

CBOW is usually faster because it predicts one word from many context words.

What is the main output of Word2Vec?

A Word frequency counts

B Part-of-speech tags

C Word vectors (embeddings)

D Sentence summaries

Word2Vec outputs word vectors that capture word meanings.

Which model is better for rare words?

A CBOW

B Skip-gram

C Both perform the same

D Neither handles rare words

Skip-gram works better with rare words by focusing on predicting context from the target word.

In CBOW, what is used to predict the target word?

A The surrounding context words

B Random words from the corpus

C The entire sentence

D The target word itself

CBOW uses the surrounding words to predict the missing target word.

Describe how the CBOW and Skip-gram models work in Word2Vec and their main differences.

Think about which words are inputs and which are outputs in each model.

Explain why Word2Vec embeddings are useful for language tasks.

Consider how turning words into numbers helps machines.

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