Stopword removal in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to remove stopwords from the list of words.

NLP

filtered_words = [word for word in words if word not in [1]]

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Astopwords

Bpunctuation

Cvocabulary

Dtokens

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tokens' instead of 'stopwords' causes wrong filtering.

Using 'punctuation' removes symbols, not stopwords.

The variable stopwords contains common words to remove. We keep words not in stopwords.

2fill in blank

medium

Complete the code to import the stopwords list from NLTK.

NLP

from nltk.corpus import [1]

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Awords

Bstopwords

Ctokenize

Dcorpus

Attempts:

3 left

💡 Hint

Common Mistakes

Importing 'words' instead of 'stopwords' causes attribute errors.

Importing 'tokenize' is unrelated to stopword lists.

We import stopwords from nltk.corpus to access common stopwords.

3fill in blank

hard

Fix the error in the code to get English stopwords from NLTK.

NLP

stop_words = set(stopwords.words([1]))

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A'eng'

B'English'

C'en'

D'english'

Attempts:

3 left

💡 Hint

Common Mistakes

Using capitalized 'English' causes a KeyError.

Using abbreviations like 'eng' or 'en' are invalid.

The correct language code for English stopwords in NLTK is 'english' all lowercase.

4fill in blank

hard

Fill both blanks to create a list of words without stopwords from a sentence.

NLP

filtered = [[1] for [2] in sentence.split() if [1] not in stop_words]

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Aword

Bsentence

Cstop_words

Dwords

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names in the loop and condition causes errors.

Using 'sentence' or 'words' instead of 'word' is incorrect.

We iterate over each word in the sentence split by spaces, and keep words not in stop_words.

5fill in blank

hard

Fill all three blanks to create a dictionary of word counts excluding stopwords.

NLP

word_counts = [1](word for word in words if word not in [2])
filtered_counts = {word: count for word, count in word_counts.[3]()}

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ACounter

Bstop_words

Citems

Ddict

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'dict' instead of 'Counter' loses counting functionality.

Using 'keys()' instead of 'items()' misses counts.

We use Counter to count words excluding stop_words. Then we create a dictionary from word_counts.items().