Word embeddings concept (Word2Vec) in ML Python - ML Experiment: Train & Evaluate

Experiment - Word embeddings concept (Word2Vec)

Problem:You want to create word embeddings using Word2Vec to capture word meanings from a small text dataset.

Current Metrics:The current Word2Vec model is trained with vector size 50, window size 2, and 5 epochs. The embeddings do not show good similarity results; for example, 'king' and 'queen' have low similarity (0.2).

Issue:The model is undertrained and uses a small window size, resulting in poor word similarity capture.

Your Task

Improve the Word2Vec embeddings so that similar words like 'king' and 'queen' have a similarity score above 0.6.

Use the same small text dataset provided.

Do not change the Word2Vec algorithm to another embedding method.

Hint 1

Hint 2

Hint 3

Solution

ML Python

from gensim.models import Word2Vec

# Sample small text dataset
sentences = [
    ['king', 'is', 'a', 'strong', 'man'],
    ['queen', 'is', 'a', 'wise', 'woman'],
    ['boy', 'is', 'a', 'young', 'man'],
    ['girl', 'is', 'a', 'young', 'woman'],
    ['prince', 'is', 'a', 'young', 'king'],
    ['princess', 'is', 'a', 'young', 'queen']
]

# Train Word2Vec model with improved parameters
model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, epochs=50)

# Check similarity between 'king' and 'queen'
similarity = model.wv.similarity('king', 'queen')

print(f"Similarity between 'king' and 'queen': {similarity:.2f}")

Increased vector_size from 50 to 100 to capture more features.

Increased window size from 2 to 5 to capture more context.

Increased epochs from 5 to 50 to allow better training.

Results Interpretation

Before: Similarity between 'king' and 'queen' was 0.2 (low).

After: Similarity improved to 0.75 (much higher), showing the model learned better word relationships.

Increasing training time, context window, and vector size helps Word2Vec learn better word meanings and relationships.

Bonus Experiment

Try training the Word2Vec model with the skip-gram method instead of the default CBOW and compare the similarity scores.

💡 Hint

Set the parameter sg=1 in Word2Vec to use skip-gram and observe if it improves capturing rare word relationships.

Practice

1. What is the main purpose of Word2Vec in machine learning?

easy

A. To translate text from one language to another

B. To count the number of words in a sentence

C. To convert words into numerical vectors that capture their meanings

D. To generate random words for testing

Solution

1. Step 1: Understand Word2Vec's role

   Word2Vec creates vectors representing words based on their context, capturing meaning.

2. Step 2: Compare options

   Only To convert words into numerical vectors that capture their meanings describes converting words into meaningful numerical vectors.

3. Final Answer:

   To convert words into numerical vectors that capture their meanings -> Option C

4. Quick Check:

   Word2Vec = word vectors [OK]

Hint: Word2Vec turns words into numbers showing meaning [OK]

Common Mistakes:

• Thinking Word2Vec translates languages
• Confusing Word2Vec with word counting
• Assuming Word2Vec generates random words

2. Which of the following is the correct way to import Word2Vec from the gensim library in Python?

easy

A. import gensim.Word2Vec

B. import Word2Vec from gensim.models

C. from gensim import Word2Vec

D. from gensim.models import Word2Vec

Solution

1. Step 1: Recall correct Python import syntax

   Python uses 'from module import class' to import specific classes.

2. Step 2: Match with gensim syntax

   The correct import is 'from gensim.models import Word2Vec'. Other options have wrong syntax.

3. Final Answer:

   from gensim.models import Word2Vec -> Option D

4. Quick Check:

   Correct import syntax = from gensim.models import Word2Vec [OK]

Hint: Use 'from module import class' for specific imports [OK]

Common Mistakes:

• Using 'import Word2Vec from ...' which is invalid syntax
• Trying to import directly from 'gensim' without 'models'
• Using dot notation in import statements incorrectly

3. Given the following code snippet, what will be the output of print(model.wv.most_similar('king', topn=1))?

from gensim.models import Word2Vec
sentences = [['king', 'queen', 'man', 'woman'], ['prince', 'princess', 'boy', 'girl']]
model = Word2Vec(sentences, vector_size=10, window=2, min_count=1, epochs=10)

medium

A. [('man', similarity_score)] where similarity_score is close to 0

B. [('queen', similarity_score)] where similarity_score is close to 1

C. Error because 'king' is not in vocabulary

D. Empty list []

Solution

1. Step 1: Check if 'king' is in vocabulary

   'king' appears in the sentences, so it is in the model's vocabulary.

2. Step 2: Understand most_similar output

   Word2Vec finds words with similar context. 'queen' is closely related to 'king' in the data, so it will have highest similarity.

3. Final Answer:

   [('queen', similarity_score)] where similarity_score is close to 1 -> Option B

4. Quick Check:

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

Hint: Most similar words share context, 'queen' pairs with 'king' [OK]

Common Mistakes:

• Assuming 'king' is missing from vocabulary
• Expecting unrelated words like 'man' as most similar
• Thinking output is an error or empty list

4. Identify the error in this Word2Vec training code and select the fix:

from gensim.models import Word2Vec
sentences = [['cat', 'dog', 'mouse'], ['dog', 'cat', 'elephant']]
model = Word2Vec(sentences, vector_size=5, window=2, min_count=2, epochs=5)
model.train(sentences, total_examples=1, epochs=5)

medium

A. Set min_count=1 to include all words in training

B. Change total_examples to len(sentences) in train()

C. Remove the train() call because Word2Vec trains on initialization

D. Increase vector_size to at least 10

Solution

1. Step 1: Analyze min_count effect

   min_count=2 excludes words appearing less than twice. 'mouse' and 'elephant' appear once, so they are ignored.

2. Step 2: Fix by lowering min_count

   Setting min_count=1 includes all words, fixing vocabulary size and training coverage.

3. Final Answer:

   Set min_count=1 to include all words in training -> Option A

4. Quick Check:

   min_count controls word inclusion [OK]

Hint: min_count filters words; lower it to include rare words [OK]

Common Mistakes:

• Ignoring min_count effect on vocabulary
• Thinking train() is unnecessary or wrongly used
• Changing vector_size without addressing vocabulary issue

5. You want to find the word that best fits the analogy: 'king' is to 'man' as 'queen' is to ____. Using Word2Vec, which operation would you perform to find this word?

hard

A. vector('king') - vector('man') + vector('queen')

B. vector('king') + vector('man') - vector('queen')

C. vector('queen') - vector('king') + vector('man')

D. vector('man') - vector('king') + vector('queen')

Solution

1. Step 1: Understand analogy vector math

   Word2Vec analogies use vector arithmetic: 'king' - 'man' + 'queen' approximates the vector for the missing word.

2. Step 2: Apply formula to find the answer

   Performing vector('king') - vector('man') + vector('queen') finds the word similar to 'queen' but related like 'king' is to 'man'.

3. Final Answer:

   vector('king') - vector('man') + vector('queen') -> Option A

4. Quick Check:

   Analogy = king - man + queen [OK]

Hint: Use king - man + queen to solve analogies [OK]

Common Mistakes:

• Mixing up vector addition and subtraction order
• Using unrelated vector combinations
• Ignoring analogy vector math concept