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]

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

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}

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

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 >.

Practice

1. What is the main purpose of Natural Language Processing (NLP)?

easy

A. To design computer graphics

B. To help computers understand and work with human language

C. To create new programming languages

D. To improve computer hardware speed

Solution

1. Step 1: Understand NLP's role

   NLP focuses on making computers understand human language, like English or Spanish.

2. Step 2: Compare options

   Only To help computers understand and work with human language talks about understanding human language, which is the core of NLP.

3. Final Answer:

   To help computers understand and work with human language -> Option B

4. Quick Check:

   NLP = Understanding human language [OK]

Hint: NLP = computers + human language understanding [OK]

Common Mistakes:

• Confusing NLP with hardware improvements
• Thinking NLP creates programming languages
• Mixing NLP with graphic design

2. Which of the following is the correct way to represent a sentence as a list of words in Python for NLP?

easy

A. sentence = ["Hello", "world"]

B. sentence = "Hello world"

C. sentence = "Hello, world"

D. sentence = {"Hello", "world"}

Solution

1. Step 1: Understand data structures for words

   In Python, a list [] holds ordered items like words in a sentence.

2. Step 2: Check options

   sentence = ["Hello", "world"] uses a list of words, which is correct for NLP tasks needing word tokens.

3. Final Answer:

   sentence = ["Hello", "world"] -> Option A

4. Quick Check:

   List of words = sentence = ["Hello", "world"] [OK]

Hint: Words in NLP are stored as lists, not strings or sets [OK]

Common Mistakes:

• Using a string instead of a list for tokens
• Using curly braces which create sets, not lists
• Confusing punctuation inside strings

3. Given the Python code below, what will be the output?

text = "I love NLP"
tokens = text.split()
print(len(tokens))

medium

A. 3

B. 2

C. 1

D. 4

Solution

1. Step 1: Understand the split() method

   The split() method splits the string into words separated by spaces, so "I love NLP" becomes ["I", "love", "NLP"].

2. Step 2: Count the tokens

   There are 3 words, so len(tokens) returns 3.

3. Final Answer:

   3 -> Option A

4. Quick Check:

   Split words count = 3 [OK]

Hint: Count words after split() to get token length [OK]

Common Mistakes:

• Counting characters instead of words
• Forgetting split() splits by spaces
• Assuming punctuation affects split count

4. Find the error in the following Python code for tokenizing a sentence:

sentence = "Hello, world!"
tokens = sentence.split(',')
print(tokens)

medium

A. The split method does not exist for strings

B. The sentence variable should be a list, not string

C. The print statement is missing parentheses

D. The split should be on space, not comma

Solution

1. Step 1: Analyze the split delimiter

   The code splits the sentence on commas, but the sentence has a comma and an exclamation mark, so splitting on comma alone leaves ' world!' with punctuation.

2. Step 2: Correct the split delimiter

   To get clean tokens, splitting on space ' ' is better for this sentence.

3. Final Answer:

   The split should be on space, not comma -> Option D

4. Quick Check:

   Split delimiter must match word separators [OK]

Hint: Split on spaces to separate words, not commas [OK]

Common Mistakes:

• Using wrong delimiter for split
• Thinking split() is missing or invalid
• Confusing print syntax in Python 3

5. Which of the following best explains why NLP is important for bridging humans and computers?

hard

A. NLP speeds up computer processors to handle more data

B. NLP creates new programming languages for developers

C. NLP allows computers to process and understand human language, enabling applications like chatbots and translation

D. NLP designs user interfaces for better graphics

Solution

1. Step 1: Identify NLP's role in communication

   NLP helps computers understand human language, which is key to making computers interact naturally with people.

2. Step 2: Match with real-world applications

   Applications like chatbots and translation rely on NLP to work well.

3. Final Answer:

   NLP allows computers to process and understand human language, enabling applications like chatbots and translation -> Option C

4. Quick Check:

   NLP = human language understanding for apps [OK]

Hint: NLP = computers understanding human language for apps [OK]

Common Mistakes:

• Confusing NLP with hardware or UI design
• Thinking NLP creates programming languages
• Ignoring NLP's role in communication