Why preprocessing cleans raw text in NLP - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert raw text to lowercase for consistent processing.

NLP

clean_text = raw_text.[1]()

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Acapitalize

Btitle

Clower

Dupper

Attempts:

3 left

💡 Hint

Common Mistakes

Using upper() changes text to uppercase, which is not standard for preprocessing.

Using lower() converts all characters to lowercase, making text uniform and easier to analyze.

2fill in blank

medium

Complete the code to remove punctuation from the text.

NLP

import string
clean_text = ''.join(char for char in raw_text if char not in [1])

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Astring.punctuation

Bstring.whitespace

Cstring.ascii_letters

Dstring.digits

Attempts:

3 left

💡 Hint

Common Mistakes

Removing digits or whitespace instead does not clean punctuation.

string.punctuation contains all punctuation characters, so removing them cleans the text.

3fill in blank

hard

Fix the error in the code to tokenize text into words correctly.

NLP

tokens = raw_text.[1](' ')

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Astrip

Bjoin

Creplace

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using join() combines tokens instead of splitting.

split(' ') breaks the text into words by spaces, which is basic tokenization.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts from tokenized text.

NLP

word_counts = {}
for word in tokens:
    word_counts[[1]] = word_counts.get([2], 0) + 1

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Aword

Btoken

Cword.lower()

Dtokens

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'token' or 'tokens' causes errors because those variables don't exist or are collections.

Using word as the key counts each word's occurrences correctly.

5fill in blank

hard

Fill all three blanks to filter out stopwords from tokens and create a clean list.

NLP

stopwords = {'and', 'the', 'is', 'in'}
clean_tokens = [[1] for [2] in tokens if [3] not in stopwords]

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Aword

Btoken

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names causes NameError or incorrect filtering.

Use token as the loop variable and filter tokens not in stopwords, then keep them as token.