Why similarity measures find related text in NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate cosine similarity between two vectors.

NLP

from numpy import dot
from numpy.linalg import norm

def cosine_similarity(vec1, vec2):
    return dot(vec1, vec2) / (norm(vec1) [1] norm(vec2))

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

A+

B*

C-

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using addition instead of multiplication in the denominator.

Cosine similarity divides the dot product by the product of the norms (lengths) of the two vectors.

2fill in blank

medium

Complete the code to convert text into a vector using term frequency.

NLP

def term_frequency(text, word):
    words = text.lower().split()
    return words.count([1]) / len(words)

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

Aword

Btext

Cwords

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using the whole text or list instead of the target word.

We count how many times the specific word appears in the text.

3fill in blank

hard

Fix the error in the code to compute Jaccard similarity between two sets.

NLP

def jaccard_similarity(set1, set2):
    intersection = set1.intersection(set2)
    union = set1.[1](set2)
    return len(intersection) / len(union)

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

Adifference

Bintersect

Cunion

Dadd

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'intersect' or 'add' which are not valid set methods.

The union of two sets is found with the union method.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts for words longer than 3 letters.

NLP

text = 'machine learning finds related text'
words = text.split()
word_counts = {word: words.count(word) for word in words if len(word) [1] [2]

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

A>

B<

C3

D4

Attempts:

3 left

💡 Hint

Common Mistakes

Using less than instead of greater than.

Using wrong length number.

We want words longer than 3 letters, so length must be greater than 3.

5fill in blank

hard

Fill all three blanks to filter words and create a dictionary with uppercase keys and counts for words longer than 4 letters.

NLP

text = 'similarity measures find related text'
words = text.split()
filtered_counts = [1]: [2] for word in words if len(word) [3] 4

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

Aword.upper()

Bword

C>

Dwords.count(word)

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase keys.

Using wrong comparison operator.

Keys are uppercase words, values are counts, and filter is for words longer than 4 letters.

Practice

1. Why do similarity measures help find related text in NLP?

easy

A. Because they compare numeric representations of texts to find closeness

B. Because they translate text into images for comparison

C. Because they count the number of words in each text

D. Because they randomly select texts to compare

Solution

1. Step 1: Understand text representation in NLP

   Texts are converted into numbers (vectors) so computers can compare them easily.

2. Step 2: Role of similarity measures

   Similarity measures calculate how close these numeric vectors are, showing relatedness.

3. Final Answer:

   Because they compare numeric representations of texts to find closeness -> Option A

4. Quick Check:

   Similarity = Numeric comparison [OK]

Hint: Similarity means comparing numbers, not words directly [OK]

Common Mistakes:

• Thinking similarity compares raw words directly
• Confusing similarity with random selection
• Believing similarity translates text into images

2. Which of the following is the correct way to calculate cosine similarity between two vectors A and B in Python?

easy

A. cos_sim = np.linalg.norm(A - B)

B. cos_sim = np.sum(A + B)

C. cos_sim = np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B))

D. cos_sim = np.dot(A, B) * (np.linalg.norm(A) + np.linalg.norm(B))

Solution

1. Step 1: Recall cosine similarity formula

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

2. Step 2: Match formula to code

   cos_sim = np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) matches this formula exactly using numpy functions.

3. Final Answer:

   cos_sim = np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) -> Option C

4. Quick Check:

   Cosine similarity formula = cos_sim = np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B)) [OK]

Hint: Cosine similarity = dot product ÷ product of norms [OK]

Common Mistakes:

• Adding vectors instead of dot product
• Multiplying dot product by sum of norms
• Using norm of difference instead of cosine similarity

3. Given two texts converted to sets of words: text1 = {'apple', 'banana', 'cherry'} and text2 = {'banana', 'cherry', 'date'}, what is the Jaccard similarity between them?

medium

A. 0.25

B. 0.6

C. 0.75

D. 0.5

Solution

1. Step 1: Calculate intersection and union of sets

   Intersection = {'banana', 'cherry'} (2 items), Union = {'apple', 'banana', 'cherry', 'date'} (4 items).

2. Step 2: Compute Jaccard similarity

   Jaccard similarity = size of intersection ÷ size of union = 2 ÷ 4 = 0.5.

3. Final Answer:

   0.5 -> Option D

4. Quick Check:

   Jaccard = intersection/union = 0.5 [OK]

Hint: Jaccard = common words ÷ total unique words [OK]

Common Mistakes:

• Counting union incorrectly
• Using sum instead of division
• Confusing intersection with union size

4. The following Python code tries to compute cosine similarity but gives an error. What is the main issue?

import numpy as np
A = np.array([1, 2, 3])
B = np.array([4, 5])
cos_sim = np.dot(A, B) / (np.linalg.norm(A) * np.linalg.norm(B))
print(cos_sim)

medium

A. np.linalg.norm is used incorrectly

B. Vectors A and B have different lengths causing dot product error

C. Division by zero error

D. Missing import statement for numpy

Solution

1. Step 1: Check vector sizes

   Vector A has length 3, vector B has length 2, so dot product is invalid.

2. Step 2: Understand dot product requirements

   Dot product requires vectors of same length; mismatch causes error.

3. Final Answer:

   Vectors A and B have different lengths causing dot product error -> Option B

4. Quick Check:

   Dot product needs equal length vectors [OK]

Hint: Dot product needs vectors of same length [OK]

Common Mistakes:

• Assuming norm causes error
• Thinking division by zero happened
• Ignoring vector length mismatch

5. You want to find related news articles using similarity measures. Which approach best improves accuracy when articles have different lengths and some common words?

hard

A. Use cosine similarity on TF-IDF vectors to reduce common word impact

B. Use raw word counts and Jaccard similarity without preprocessing

C. Compare articles by counting total words only

D. Use random similarity scores to guess relatedness

Solution

1. Step 1: Understand TF-IDF role

   TF-IDF reduces weight of common words, highlighting unique terms in articles.

2. Step 2: Why cosine similarity on TF-IDF helps

   Cosine similarity measures angle between vectors, handling different lengths well.

3. Final Answer:

   Use cosine similarity on TF-IDF vectors to reduce common word impact -> Option A

4. Quick Check:

   TF-IDF + cosine similarity = better relatedness [OK]

Hint: TF-IDF + cosine similarity handles length and common words best [OK]

Common Mistakes:

• Ignoring word importance by using raw counts
• Using Jaccard without preprocessing
• Relying on random scores