Jaccard similarity in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the 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'

Aunion

Bdifference

Csymmetric_difference

Dadd

Attempts:

3 left

💡 Hint

Common Mistakes

Using difference instead of union causes incorrect denominator.

Using symmetric_difference excludes common elements, which is wrong.

The Jaccard similarity is the size of the intersection divided by the size of the union of two sets. Here, union method returns the union of two sets.

2fill in blank

medium

Complete the code to convert two sentences into sets of words for Jaccard similarity calculation.

NLP

def sentence_to_set(sentence):
    words = sentence.lower().split()
    return set([1])

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Awords

Bsentence

Csentence.split()

Dsentence.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the whole sentence string instead of the list of words to set().

Not converting to lowercase before splitting causes case mismatches.

We first split the sentence into words and then convert the list words into a set to remove duplicates.

3fill in blank

hard

Fix the error in the Jaccard similarity function to avoid division by zero.

NLP

def jaccard_similarity(set1, set2):
    intersection = set1.intersection(set2)
    union = set1.union(set2)
    if len(union) == 0:
        return [1]
    return len(intersection) / len(union)

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Alen(intersection)

BNone

C1

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Returning None causes errors when using the result.

Returning 1 incorrectly suggests full similarity for empty sets.

If both sets are empty, their union length is zero. Returning 0 avoids division by zero and logically means no similarity.

4fill in blank

hard

Fill both blanks to create a function that computes Jaccard similarity from two sentences.

NLP

def jaccard_from_sentences(s1, s2):
    set1 = set(s1.lower().[1]())
    set2 = set(s2.lower().[2]())
    intersection = set1.intersection(set2)
    union = set1.union(set2)
    if len(union) == 0:
        return 0
    return len(intersection) / len(union)

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Asplit

Bstrip

Creplace

Djoin

Attempts:

3 left

💡 Hint

Common Mistakes

Using strip() removes whitespace but does not split words.

Using replace() or join() does not create word lists.

We use split() to break sentences into words before converting to sets.

5fill in blank

hard

Fill all three blanks to create a dictionary of Jaccard similarities for a list of sentence pairs.

NLP

def batch_jaccard(pairs):
    results = {}
    for i, (s1, s2) in enumerate(pairs):
        set1 = set(s1.lower().[1]())
        set2 = set(s2.lower().[2]())
        intersection = set1.[3](set2)
        union = set1.union(set2)
        if len(union) == 0:
            results[i] = 0
        else:
            results[i] = len(intersection) / len(union)
    return results

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

Asplit

Bstrip

Cintersection

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using strip() instead of split() results in wrong sets.

Using replace() or other methods that do not create word lists.

Using union instead of intersection for common elements.

We split sentences into words, then use intersection to find common words for Jaccard similarity.