What NLP actually does - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to tokenize the sentence into words.

NLP

words = sentence.[1]()

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

Astrip

Bjoin

Creplace

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using join() which combines words instead of splitting.

Using replace() which changes characters but does not split.

Using strip() which removes spaces only at ends.

The split() method breaks a sentence into words by spaces, which is the basic step in NLP called tokenization.

2fill in blank

medium

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

NLP

lower_words = [word.[1]() for word in words]

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

Acapitalize

Blower

Cupper

Dtitle

Attempts:

3 left

💡 Hint

Common Mistakes

Using upper() which makes letters uppercase.

Using capitalize() which only changes the first letter.

Using title() which capitalizes first letters of words.

Using lower() converts all letters to lowercase, helping NLP models treat words like 'Apple' and 'apple' the same.

3fill in blank

hard

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

NLP

import string
clean_words = [word.strip(string.[1]) for word in lower_words]

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

Apunctuation

Bdigits

Cwhitespace

Dascii_letters

Attempts:

3 left

💡 Hint

Common Mistakes

Using digits which removes numbers, not punctuation.

Using whitespace which removes spaces, not punctuation.

Using ascii_letters which removes letters, not punctuation.

The string.punctuation constant contains all punctuation characters, which strip() removes from the start and end of each word.

4fill in blank

hard

Fill both blanks to create a dictionary counting word frequencies.

NLP

word_counts = {word: words.[1](word) for word in [2]

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

Acount

Bindex

Cset

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using index which finds position, not count.

Using list which includes duplicates, causing repeated keys.

Using count with list keys causing repeated counting.

Using count counts how many times each word appears. Using set gets unique words to avoid repeated counting.

5fill in blank

hard

Fill all three blanks to filter words longer than 3 letters and create a frequency dictionary.

NLP

filtered_counts = {word: words.[1](word) for word in [2] if len(word) [3] 3}

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

Acount

Bset

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' which filters shorter words.

Using list instead of set causing duplicate keys.

Using index instead of count causing errors.

This code counts words using count, iterates over unique words with set, and filters words longer than 3 letters using >.

Practice

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

easy

A. To help computers understand and work with human language

B. To create images from text descriptions

C. To speed up computer hardware

D. To store large amounts of data efficiently

Solution

1. Step 1: Understand NLP's purpose

   NLP focuses on making computers understand human language, like speech or text.

2. Step 2: Compare options

   Only To help computers understand and work with human language describes this goal; others are unrelated to language understanding.

3. Final Answer:

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

4. Quick Check:

   NLP goal = Understand human language [OK]

Hint: NLP = computers understanding human language [OK]

Common Mistakes:

• Confusing NLP with image processing
• Thinking NLP is about hardware or storage
• Mixing NLP with unrelated computer tasks

2. Which of the following is a correct step in basic NLP processing?

easy

A. Compiling code into machine language

B. Splitting text into words or sentences

C. Encrypting data for security

D. Formatting images for display

Solution

1. Step 1: Identify NLP preprocessing steps

   Basic NLP starts by breaking text into smaller parts like words or sentences.

2. Step 2: Eliminate unrelated options

   Options B, C, and D relate to programming, security, or images, not NLP text processing.

3. Final Answer:

   Splitting text into words or sentences -> Option B

4. Quick Check:

   Basic NLP step = Text splitting [OK]

Hint: NLP starts by breaking text into pieces [OK]

Common Mistakes:

• Confusing NLP steps with programming tasks
• Mixing text processing with encryption or image tasks
• Choosing unrelated computer operations

3. Given this Python code using NLP, what will be the output?

import nltk
text = "Hello world!"
tokens = nltk.word_tokenize(text)
print(tokens)

medium

A. ['Hello world!']

B. Error: nltk module not found

C. ['Hello_world!']

D. ['Hello', 'world', '!']

Solution

1. Step 1: Understand nltk.word_tokenize function

   This function splits text into words and punctuation marks as separate tokens.

2. Step 2: Apply tokenization to the text

   "Hello world!" becomes ['Hello', 'world', '!'] as separate tokens.

3. Final Answer:

   ['Hello', 'world', '!'] -> Option D

4. Quick Check:

   Tokenize "Hello world!" = ['Hello', 'world', '!'] [OK]

Hint: Tokenize splits words and punctuation separately [OK]

Common Mistakes:

• Expecting the whole sentence as one token
• Ignoring punctuation as separate tokens
• Assuming code will error without nltk installed

4. Find the error in this NLP code snippet:

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

medium

A. Calling lower() on a list instead of a string

B. Using split() instead of word_tokenize()

C. Missing import statement for nltk

D. No error, code runs fine

Solution

1. Step 1: Analyze the code operations

   text.split() returns a list of words, but tokens.lower() tries to call lower() on a list.

2. Step 2: Identify the error type

   Lists do not have a lower() method, causing an AttributeError.

3. Final Answer:

   Calling lower() on a list instead of a string -> Option A

4. Quick Check:

   lower() on list causes error [OK]

Hint: lower() works on strings, not lists [OK]

Common Mistakes:

• Thinking split() is wrong here
• Ignoring that lower() is called on a list
• Assuming code runs without error

5. You want to build a chatbot that understands user questions and answers them. Which NLP steps should you include?

hard

A. Database indexing, query optimization, and caching

B. Image resizing, color correction, and pixel filtering

C. Tokenization, part-of-speech tagging, named entity recognition, and intent detection

D. Hardware acceleration, memory management, and threading

Solution

1. Step 1: Identify NLP tasks for chatbot understanding

   Tokenization breaks text into words, POS tagging finds word roles, named entity recognition finds names, and intent detection understands user goals.

2. Step 2: Eliminate unrelated options

   Options A, B, and D relate to databases, images, or hardware, not language understanding.

3. Final Answer:

   Tokenization, part-of-speech tagging, named entity recognition, and intent detection -> Option C

4. Quick Check:

   Chatbot NLP steps = Tokenize + Tag + Recognize + Detect intent [OK]

Hint: Chatbots need tokenizing, tagging, recognizing, and intent detection [OK]

Common Mistakes:

• Confusing NLP with image or hardware tasks
• Ignoring intent detection for understanding
• Choosing unrelated computer processes