Natural language processing basics in Intro to Computing - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What does tokenization do in NLP?

In natural language processing, what is the main purpose of tokenization?

A It converts text into numerical vectors directly.

B It translates text from one language to another.

C It removes all punctuation from the text.

D It splits text into smaller pieces like words or sentences.

💡 Hint

Think about how you break a sentence into parts before understanding it.

Tokenization breaks text into smaller units called tokens, usually words or sentences, which are easier to analyze.

🔍 Analysis

intermediate

Output of simple word count code

What is the output of this Python code that counts word frequencies?

Intro to Computing

from collections import Counter
text = 'apple banana apple orange banana apple'
word_counts = Counter(text.split())
print(word_counts)

A Counter({'apple': 3, 'banana': 2, 'orange': 1})

B Counter({'banana': 3, 'apple': 2, 'orange': 1})

C {'apple': 3, 'banana': 2, 'orange': 1}

D Counter({'apple': 1, 'banana': 1, 'orange': 1})

💡 Hint

Count how many times each word appears in the text.

The Counter counts each word's frequency: 'apple' appears 3 times, 'banana' 2 times, and 'orange' once.

❓ Model Choice

advanced

Best model for sentiment analysis

You want to build a model to classify movie reviews as positive or negative. Which model type is best suited for this task?

A Convolutional Neural Network (CNN) designed for image recognition

B K-Means clustering for grouping similar reviews

C Recurrent Neural Network (RNN) or Transformer model for text sequences

D Linear regression predicting review length

💡 Hint

Think about models that understand sequences of words.

RNNs and Transformers are designed to process sequences like text, making them suitable for sentiment analysis.

❓ Metrics

advanced

Choosing the right metric for imbalanced text classification

You trained a spam detection model where spam messages are rare. Which metric is best to evaluate your model's performance?

A Precision and recall, to balance false positives and false negatives

B Accuracy, because it shows overall correct predictions

C Mean squared error, to measure prediction errors

D Confusion matrix size, to count total classes

💡 Hint

Think about what matters when one class is much smaller than the other.

Precision and recall help measure how well the model detects rare spam messages without too many mistakes.

🔍 Analysis

expert

Why does this text preprocessing code raise an error?

Consider this Python code snippet for lowercasing and removing punctuation from text. Why does it raise an error?

Intro to Computing

import string
text = 'Hello, World!'
clean_text = text.lower().translate(str.maketrans('', '', string.punctuation))
print(clean_text)

A lower() cannot be chained with translate(), causing an AttributeError

B translate() expects a translation table, but string.punctuation is a string, causing a TypeError

C string.punctuation is empty, so translate() does nothing

D print() is missing parentheses, causing a SyntaxError

💡 Hint

Check what the translate() method needs as input.

translate() requires a translation table created by str.maketrans(), not a string like string.punctuation.

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 encrypt messages for security

Solution

1. Step 1: Understand NLP purpose

   NLP is about 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 computers working with human language, which matches NLP's goal.

3. Final Answer:

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

4. Quick Check:

   NLP = computers understand human language [OK]

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

Common Mistakes:

• Confusing NLP with image processing
• Thinking NLP speeds up hardware
• Mixing NLP with encryption tasks

2. Which of the following is the correct way to split a sentence into words in Python?

easy

A. sentence.add()

B. sentence.split()

C. sentence.remove()

D. sentence.join()

Solution

1. Step 1: Recall Python string methods

   To split a sentence into words, we use the split() method which breaks text at spaces by default.

2. Step 2: Check each option

   Only split() breaks text into parts; join() combines parts, remove() and add() are not string methods.

3. Final Answer:

   sentence.split() -> Option B

4. Quick Check:

   Split = break text into words [OK]

Hint: Use split() to break text into words [OK]

Common Mistakes:

• Using join() instead of split()
• Trying to use remove() on strings
• Confusing add() with string methods

3. What will be the output of this Python code?

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

medium

A. 1

B. 0

C. 4

D. 3

Solution

1. Step 1: Tokenize the text

   Using split() on "I love AI" splits it into ['I', 'love', 'AI'], which has 3 words.

2. Step 2: Count tokens

   len(tokens) counts the number of items in the list, which is 3.

3. Final Answer:

   3 -> Option D

4. Quick Check:

   Words count = 3 [OK]

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

Common Mistakes:

• Counting characters instead of words
• Assuming split() returns a string
• Forgetting split() splits by spaces

4. Find the error in this code snippet for tokenizing text:

text = "Hello world"
tokens = text.split('')
print(tokens)

medium

A. text should be a list, not a string

B. print() is missing parentheses

C. split('') is invalid; split() should have no argument or a space

D. tokens should be joined, not split

Solution

1. Step 1: Check split() argument

   split('') is invalid in Python because empty string is not a valid separator.

2. Step 2: Correct usage

   Use split() with no argument or split(' ') to split by spaces correctly.

3. Final Answer:

   split('') is invalid; split() should have no argument or a space -> Option C

4. Quick Check:

   split('') causes error [OK]

Hint: split('') causes error; use split() or split(' ') [OK]

Common Mistakes:

• Using empty string as split separator
• Thinking print() needs no parentheses in Python 3
• Confusing string and list types

5. You want to count how many times each word appears in a sentence using Python. Which code correctly does this?

hard

A. from collections import Counter
words = sentence.split()
counts = Counter(words)
print(counts)

B. words = sentence.join()
counts = len(words)
print(counts)

C. counts = sentence.count()
print(counts)

D. words = sentence.split('')
counts = dict(words)
print(counts)

Solution

1. Step 1: Tokenize sentence into words

   sentence.split() breaks the sentence into a list of words.

2. Step 2: Use Counter to count words

   Counter(words) counts how many times each word appears and stores results in a dictionary-like object.

3. Step 3: Check other options

   Options A, B, D use wrong methods or syntax for counting words.

4. Final Answer:

   from collections import Counter words = sentence.split() counts = Counter(words) print(counts) -> Option A

5. Quick Check:

   Use Counter + split() for word counts [OK]

Hint: Use Counter with split() to count words fast [OK]

Common Mistakes:

• Using join() instead of split()
• Calling count() without arguments
• Trying to split with empty string