Challenges in language processing in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to tokenize the sentence into words.

NLP

tokens = sentence.[1]()

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Asplit

Bjoin

Creplace

Dstrip

Attempts:

3 left

💡 Hint

Common Mistakes

Using join() instead of split()

Using replace() which changes characters

The split() method breaks a sentence into words by spaces, which is basic tokenization.

2fill in blank

medium

Complete the code to convert all tokens to lowercase for uniformity.

NLP

tokens = [word.[1]() for word in tokens]

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Alower

Bcapitalize

Cupper

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using upper() which makes all letters uppercase

Using capitalize() which only changes first letter

The lower() method converts all characters in a string to lowercase, helping uniform text processing.

3fill in blank

hard

Fix the error in the code to remove punctuation from each token.

NLP

import string
clean_tokens = [word.strip(string.[1]) for word in tokens]

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Aascii_letters

Bwhitespace

Cpunctuation

Ddigits

Attempts:

3 left

💡 Hint

Common Mistakes

Using whitespace which removes spaces, not punctuation

Using digits which removes numbers

string.punctuation contains all punctuation characters to strip from words.

4fill in blank

hard

Fill both blanks to create a dictionary counting word frequencies.

NLP

word_freq = {word: tokens.[1](word) for word in tokens if word [2] ''}

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Acount

B!=

C==

Dappend

Attempts:

3 left

💡 Hint

Common Mistakes

Using == instead of != in condition

Using append which is for lists, not counting

count counts occurrences of each word. The condition word != '' skips empty strings.

5fill in blank

hard

Fill all three blanks to filter out stopwords and create a list of important words.

NLP

stopwords = {'and', 'the', 'is', 'in'}
important_words = [word for word in tokens if word [1] stopwords and len(word) [2] [3]]

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Anot in

B>

C2

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'in' instead of 'not in' to exclude stopwords

Using '<' instead of '>' for length condition

We exclude stopwords with not in. We keep words longer than 2 characters using len(word) > 2.