What NLP actually does - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding NLP Tasks

Which of the following best describes a primary task of Natural Language Processing (NLP)?

A Converting images into text descriptions

B Translating human language into machine-readable data for understanding and response

C Optimizing hardware performance for faster computing

D Storing large amounts of data efficiently

💡 Hint

Think about what NLP does with human language.

NLP focuses on making machines understand and work with human language by converting it into a form they can process.

❓ Predict Output

intermediate

Output of Tokenization in NLP

What is the output of this Python code using NLTK for tokenization?

NLP

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
text = "Hello world! NLP is fun."
tokens = word_tokenize(text)
print(tokens)

A ['Hello world', 'NLP is fun']

B ['Hello world!', 'NLP is fun.']

C ['Hello', 'world', '!', 'NLP', 'is', 'fun', '.']

D ['Hello', 'world!', 'NLP', 'is', 'fun.']

💡 Hint

Tokenization splits text into words and punctuation separately.

The word_tokenize function splits the sentence into words and punctuation marks as separate tokens.

❓ Model Choice

advanced

Choosing the Right NLP Model for Sentiment Analysis

Which model is best suited for analyzing the sentiment (positive or negative feeling) of movie reviews?

A A convolutional neural network (CNN) trained on labeled text data

B A k-means clustering model for grouping text

C A linear regression model predicting numerical values

D A decision tree model for image classification

💡 Hint

Sentiment analysis needs a model that understands text and classifies feelings.

CNNs can learn patterns in text sequences and are effective for sentiment classification tasks.

❓ Metrics

advanced

Evaluating NLP Classification Accuracy

You trained an NLP model to classify emails as spam or not spam. Which metric best tells you how many emails were correctly classified overall?

A Accuracy

B Recall

C Precision

D F1 Score

💡 Hint

Think about the metric that measures correct predictions out of all predictions.

Accuracy measures the proportion of total correct predictions (both spam and not spam) out of all emails.

🔧 Debug

expert

Debugging a Named Entity Recognition (NER) Model Output

Given this Python code snippet using spaCy for NER, what error or issue will occur when running it?

NLP

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Apple is looking at buying U.K. startup for $1 billion')
for ent in doc.ents:
    print(ent.text, ent.label_)

# Later code
print(doc.entities)

A TypeError because 'ent.label_' is not a string

B Prints all entities correctly without error

C SyntaxError due to missing colon in for loop

D AttributeError because 'Doc' object has no attribute 'entities'

💡 Hint

Check spaCy's documentation for the correct attribute name to access entities.

The correct attribute is 'ents', not 'entities'. Using 'doc.entities' causes an AttributeError.

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