Word similarity and analogies in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained word embedding model using gensim.

NLP

from gensim.models import KeyedVectors
model = KeyedVectors.load_word2vec_format('[1]', binary=True)

Drag options to blanks, or click blank then click option'

Aword2vec.txt

Bfasttext.vec

Cglove.6B.100d.txt

DGoogleNews-vectors-negative300.bin

Attempts:

3 left

💡 Hint

Common Mistakes

Using a text file instead of a binary file for Word2Vec.

Confusing GloVe or FastText files with Word2Vec binary format.

The GoogleNews-vectors-negative300.bin file is a common pre-trained Word2Vec binary model used with gensim.

2fill in blank

medium

Complete the code to find the top 3 words most similar to 'king' using the model.

NLP

similar_words = model.most_similar('[1]', topn=3)

Drag options to blanks, or click blank then click option'

Aking

Bqueen

Cman

Droyal

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a similar word like 'queen' instead of the target word 'king'.

Passing a concept or unrelated word.

The method most_similar expects the target word as input, here 'king'.

3fill in blank

hard

Fix the error in the analogy code to find the word that fits: 'man' is to 'king' as 'woman' is to ____.

NLP

result = model.most_similar(positive=['king', '[1]'], negative=['man'], topn=1)

Drag options to blanks, or click blank then click option'

Awoman

Blady

Cqueen

Dprince

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'queen' in positive instead of 'woman'.

Confusing positive and negative lists.

In the analogy, 'woman' is the positive word to add, while 'man' is subtracted.

4fill in blank

hard

Fill both blanks to create a dictionary of words and their similarity scores to 'computer', filtering only words with similarity greater than 0.7.

NLP

similarity_dict = {word: [1] for word, score in model.most_similar('[2]', topn=10) if score > 0.7}

Drag options to blanks, or click blank then click option'

Ascore

Bword

C'computer'

D'laptop'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'score' as dictionary value.

Using 'laptop' instead of 'computer' as target word.

We want the similarity score as value and 'computer' as the target word.

5fill in blank

hard

Fill all three blanks to create a list of words from the model's vocabulary that have length greater than 5 and contain the letter 'a'.

NLP

filtered_words = [[1] for [2] in model.index_to_key if [3]]

Drag options to blanks, or click blank then click option'

Aword

C'a' in word and len(word) > 5

Dlen(word) > 5

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Using only length condition without checking for 'a'.

The list comprehension iterates over words, selects words with 'a' and length > 5, and collects the word itself.

Practice

1. What does word similarity measure in natural language processing?

easy

A. How close two words are in meaning using numbers

B. How often two words appear together in a sentence

C. The length difference between two words

D. The number of letters two words share

Solution

1. Step 1: Understand the concept of word similarity

   Word similarity measures how close two words are in meaning, often represented by a number like cosine similarity.

2. Step 2: Differentiate from other word properties

   Frequency or letter count does not capture meaning closeness, so those options are incorrect.

3. Final Answer:

   How close two words are in meaning using numbers -> Option A

4. Quick Check:

   Word similarity = meaning closeness [OK]

Hint: Similarity means meaning closeness, not letter or frequency count [OK]

Common Mistakes:

• Confusing similarity with word frequency
• Thinking similarity is about word length
• Assuming similarity counts shared letters

2. Which of the following is the correct way to find the cosine similarity between two word vectors vec1 and vec2 in Python using NumPy?

easy

A. np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))

B. np.dot(vec1, vec2) * (np.linalg.norm(vec1) + np.linalg.norm(vec2))

C. np.dot(vec1, vec2) - (np.linalg.norm(vec1) * np.linalg.norm(vec2))

D. np.dot(vec1, vec2) / (np.linalg.norm(vec1) + np.linalg.norm(vec2))

Solution

1. Step 1: Recall cosine similarity formula

   Cosine similarity = dot product of vectors divided by product of their norms.

2. Step 2: Match formula to code

   np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)) matches the formula exactly using np.dot and np.linalg.norm.

3. Final Answer:

   np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2)) -> Option A

4. Quick Check:

   Cosine similarity = dot / (norm1 * norm2) [OK]

Hint: Cosine similarity divides dot product by product of norms [OK]

Common Mistakes:

• Adding norms instead of multiplying
• Subtracting norms from dot product
• Multiplying dot product by sum of norms

3. Given the following word vectors:

king = [0.5, 0.8, 0.3]
queen = [0.45, 0.75, 0.35]
man = [0.6, 0.7, 0.2]
woman = [0.55, 0.65, 0.25]

What is the closest word to the vector king - man + woman?

medium

A. king

B. man

C. queen

D. woman

Solution

1. Step 1: Calculate the vector for king - man + woman

   Subtract man from king: [0.5-0.6, 0.8-0.7, 0.3-0.2] = [-0.1, 0.1, 0.1]. Add woman: [-0.1+0.55, 0.1+0.65, 0.1+0.25] = [0.45, 0.75, 0.35].

2. Step 2: Compare result to known vectors

   The resulting vector matches queen exactly: [0.45, 0.75, 0.35].

3. Final Answer:

   queen -> Option C

4. Quick Check:

   king - man + woman = queen [OK]

Hint: king - man + woman equals queen vector [OK]

Common Mistakes:

• Not subtracting man vector before adding woman
• Mixing up vector addition order
• Choosing original words instead of analogy result

4. The following code tries to find the word most similar to king - man + woman but has a flaw:

import numpy as np
words = {'king': np.array([0.5, 0.8, 0.3]), 'queen': np.array([0.45, 0.75, 0.35]), 'man': np.array([0.6, 0.7, 0.2]), 'woman': np.array([0.55, 0.65, 0.25])}
result = words['king'] - words['man'] + words['woman']
max_word = None
max_sim = -1
for word, vec in words.items():
    sim = np.dot(result, vec) / (np.linalg.norm(result) * np.linalg.norm(vec))
    if sim > max_sim:
        max_word = word
print(max_word)

What is the main flaw?

medium

A. The variable max_sim is initialized incorrectly

B. Division by zero occurs due to zero vector norm

C. The dot product is computed without normalizing vectors

D. The code does not exclude the original words from similarity search

Solution

1. Step 1: Analyze the similarity search loop

   The loop compares the result vector to all words including 'king', 'man', and 'woman' which are part of the calculation.

2. Step 2: Understand why this is problematic

   Including original words can cause the highest similarity to be the input words themselves, which is usually unwanted and can cause misleading results.

3. Final Answer:

   The code does not exclude the original words from similarity search -> Option D

4. Quick Check:

   Exclude input words to avoid bias [OK]

Hint: Exclude input words from similarity search to avoid bias [OK]

Common Mistakes:

• Assuming zero division error without checking norms
• Thinking max_sim initialization causes error
• Ignoring normalization in dot product

5. You want to find the word that fits the analogy: Paris is to France as Tokyo is to ? Using pre-trained word vectors, which approach is best to find the answer?

hard

A. Calculate vector: France - Tokyo + Paris, then find closest word vector

B. Calculate vector: Tokyo - Paris + France, then find closest word vector

C. Calculate vector: Paris + France - Tokyo, then find closest word vector

D. Calculate vector: Tokyo + Paris - France, then find closest word vector

Solution

1. Step 1: Understand analogy vector arithmetic

   Analogies use the formula: word2 - word1 + word3 to find the missing word. Here, Paris is word1, France is word2, Tokyo is word3.

2. Step 2: Apply formula to this analogy

   Calculate Tokyo - Paris + France to get the vector representing the answer.

3. Final Answer:

   Calculate vector: Tokyo - Paris + France, then find closest word vector -> Option B

4. Quick Check:

   Analogy vector = word3 - word1 + word2 [OK]

Hint: Use analogy formula: word3 - word1 + word2 [OK]

Common Mistakes:

• Swapping order of subtraction and addition
• Adding all vectors without subtraction
• Using wrong words in formula