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)

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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)

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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]

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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

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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.