Why NLP bridges humans and computers - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the main NLP library used for processing human language.

NLP

import [1]

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

Anltk

Btensorflow

Cmatplotlib

Dsklearn

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing general machine learning libraries like tensorflow or sklearn instead of an NLP-specific one.

The nltk library is a popular Python toolkit for working with human language data, making it essential for NLP tasks.

2fill in blank

medium

Complete the code to tokenize a sentence into words using NLTK.

NLP

from nltk.tokenize import word_tokenize
sentence = 'Hello world!'
tokens = [1](sentence)

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

Aparse

Bsplit

Ctokenize_words

Dword_tokenize

Attempts:

3 left

💡 Hint

Common Mistakes

Using string split which does not handle punctuation properly.

Using a non-existent function like tokenize_words.

The function word_tokenize splits a sentence into individual words or tokens, which is a basic step in NLP.

3fill in blank

hard

Fix the error in the code to convert all tokens to lowercase.

NLP

tokens = ['Hello', 'World']
lower_tokens = [token.[1]() for token in tokens]

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Aupper

Btitle

Clower

Dcapitalize

Attempts:

3 left

💡 Hint

Common Mistakes

Using upper() which makes letters uppercase instead.

Using capitalize() which only changes the first letter.

The lower() method converts strings to lowercase, which helps normalize text for NLP tasks.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

NLP

words = ['chat', 'ai', 'language', 'nlp']
lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B<=

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using <= instead of > which includes shorter words.

The dictionary comprehension maps each word to its length using len(word), but only includes words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their lengths for words longer than 2 letters.

NLP

words = ['data', 'ai', 'ml', 'python']
result = { [1]: [2] for word in words if len(word) [3] 2 }

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

Aword.upper()

Blen(word)

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key instead of uppercase.

Using <= instead of > which includes shorter words.

The dictionary comprehension uses word.upper() as keys, len(word) as values, and filters words with length greater than 2 using >.