Lowercasing and normalization in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the text to lowercase.

NLP

text = "Hello World!"
lower_text = text.[1]()

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

Alower

Bupper

Ccapitalize

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using upper() instead of lower()

Using capitalize() which only changes the first letter

The lower() method converts all characters in a string to lowercase.

2fill in blank

medium

Complete the code to remove leading and trailing spaces from the text.

NLP

text = "  Hello World!  "
clean_text = text.[1]()

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

Areplace

Bsplit

Cstrip

Djoin

Attempts:

3 left

💡 Hint

Common Mistakes

Using split() which splits the string into words

Using replace() which replaces characters inside the string

The strip() method removes spaces from the start and end of a string.

3fill in blank

hard

Fix the error in the code to normalize the text by removing punctuation.

NLP

import string
text = "Hello, World!"
normalized = text.translate(str.maketrans('', '', [1]))

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

Astring.punctuation

Bstring.whitespace

Cstring.ascii_letters

Dstring.digits

Attempts:

3 left

💡 Hint

Common Mistakes

Using string.whitespace which removes spaces instead

Using string.ascii_letters which removes letters

string.punctuation contains all punctuation characters to remove from the text.

4fill in blank

hard

Fill both blanks to create a dictionary that maps words to their lowercase forms, filtering out words shorter than 4 letters.

NLP

words = ["Apple", "is", "Good", "for", "You"]
lower_map = {word[1]: word in words if len(word) [2] 3}

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

A.lower()

B>

C>=

D.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .upper() instead of .lower()

Using '>=' instead of '>' which changes the filter condition

We use .lower() to convert words to lowercase and '>' to filter words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a normalized frequency dictionary of lowercase words longer than 2 letters.

NLP

texts = ["Hello", "world", "HELLO", "test"]
freq = {}
for word in texts:
    w = word[1]
    if len(w) [2] 2:
        freq[w] = freq.get(w, 0) [3] 1

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

A.lower()

B>

C+

D-

E*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '-' or '*' instead of '+' for counting

Using '>=' instead of '>' changing the filter

We lowercase words, filter length > 2, and add 1 to frequency count.