NLP applications in real world - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a pre-trained sentiment analysis model using Hugging Face Transformers.

NLP

from transformers import pipeline
sentiment_analyzer = pipeline([1])

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

A'translation'

B'text-classification'

C'image-classification'

D'speech-recognition'

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing pipelines unrelated to text classification like 'image-classification'.

Using pipeline names meant for other modalities like speech or translation.

The 'text-classification' pipeline is used for sentiment analysis tasks in NLP.

2fill in blank

medium

Complete the code to tokenize input text for a BERT model.

NLP

from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
tokens = tokenizer.tokenize([1])

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

A'Hello, how are you?'

B12345

C['Hello', 'how', 'are', 'you']

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list of words instead of a string.

Passing a number instead of text.

The tokenizer.tokenize method expects a string input representing the text to tokenize.

3fill in blank

hard

Fix the error in the code to correctly perform named entity recognition (NER) using a Hugging Face pipeline.

NLP

from transformers import pipeline
ner_pipeline = pipeline('ner')
results = ner_pipeline([1])

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

A'Apple is a company'

B['Apple', 'is', 'a', 'company']

C123

DNone

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list of words instead of a string.

Passing a number or None instead of text.

The NER pipeline expects a string input representing the sentence to analyze.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

NLP

{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 '>' in the condition.

We want the dictionary to map each word to its length, but only include words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their frequencies only if frequency is greater than 1.

NLP

{ [1]: [2] for [3], freq in word_freq.items() if freq > 1 }

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

Aword.upper()

Bfreq

Cword

Dfreq.upper()

Attempts:

3 left

💡 Hint

Common Mistakes

Using freq.upper() which is invalid since freq is a number.

Using 'freq' as the loop variable instead of 'word'.

The keys are uppercase words, values are frequencies, and the loop variable is 'word'.

Practice

1. Which of the following is a common real-world application of NLP?

easy

A. Calculating the area of a circle

B. Sorting numbers in ascending order

C. Translating text from one language to another

D. Storing data in a database

Solution

1. Step 1: Understand what NLP does

   NLP helps computers understand and work with human language.

2. Step 2: Match application to NLP

   Translating text involves understanding language, so it is an NLP task.

3. Final Answer:

   Translating text from one language to another -> Option C

4. Quick Check:

   NLP application = Translation [OK]

Hint: NLP deals with language tasks like translation [OK]

Common Mistakes:

• Confusing data sorting with language processing
• Thinking math calculations are NLP
• Mixing database tasks with NLP

2. Which syntax correctly represents a chatbot response function in Python?

easy

A. function chatbot_response(user_input) { return 'Hello!'; }

B. def chatbot_response user_input: return 'Hello!'

C. chatbot_response = (user_input) => 'Hello!';

D. def chatbot_response(user_input): return 'Hello! How can I help?'

Solution

1. Step 1: Identify Python function syntax

   Python functions start with 'def', have parentheses around parameters, and a colon.

2. Step 2: Check each option

   def chatbot_response(user_input): return 'Hello! How can I help?' matches Python syntax correctly; others are JavaScript or incorrect.

3. Final Answer:

   def chatbot_response(user_input): return 'Hello! How can I help?' -> Option D

4. Quick Check:

   Python function syntax = def chatbot_response(user_input): return 'Hello! How can I help?' [OK]

Hint: Python functions start with def and parentheses [OK]

Common Mistakes:

• Using JavaScript syntax in Python
• Missing parentheses or colon in function definition
• Incorrect arrow function syntax in Python

3. What will be the output of this Python code snippet for sentiment analysis?

def analyze_sentiment(text):
    if 'happy' in text:
        return 'Positive'
    elif 'sad' in text:
        return 'Negative'
    else:
        return 'Neutral'

print(analyze_sentiment('I am very happy today'))

medium

A. Negative

B. Positive

C. Neutral

D. Error

Solution

1. Step 1: Check if 'happy' is in the input text

   The input text is 'I am very happy today', which contains 'happy'.

2. Step 2: Return sentiment based on condition

   Since 'happy' is found, the function returns 'Positive'.

3. Final Answer:

   Positive -> Option B

4. Quick Check:

   Text contains 'happy' = Positive sentiment [OK]

Hint: Look for keywords in text to decide sentiment [OK]

Common Mistakes:

• Confusing 'happy' with 'sad'
• Assuming default Neutral without checking conditions
• Thinking code will cause error

4. Find the error in this Python code for summarizing text:

def summarize(text):
    sentences = text.split('. ')
    summary = sentences[0]
    return summary

print(summarize('This is sentence one. This is sentence two.'))

medium

A. The code correctly returns the first sentence as summary

B. The code will cause an IndexError

C. The split should use ',' instead of '. '

D. The return statement is missing

Solution

1. Step 1: Understand the split method

   Splitting by '. ' divides text into sentences correctly.

2. Step 2: Check the summary assignment and return

   Assigning the first sentence to summary and returning it is valid.

3. Final Answer:

   The code correctly returns the first sentence as summary -> Option A

4. Quick Check:

   Splitting and returning first sentence = Correct summary [OK]

Hint: Splitting text by '. ' extracts sentences [OK]

Common Mistakes:

• Thinking split delimiter is wrong
• Expecting error when none occurs
• Missing return statement confusion

5. You want to build a chatbot that understands user questions and replies correctly. Which combination of NLP techniques is best to start with?

hard

A. Tokenization + intent recognition + response generation

B. Image recognition + speech synthesis

C. Text summarization + translation

D. Speech recognition + sentiment analysis

Solution

1. Step 1: Identify chatbot core tasks

   A chatbot needs to understand text (tokenization), detect user intent, and generate replies.

2. Step 2: Match techniques to chatbot needs

   Tokenization breaks text into words, intent recognition finds meaning, and response generation creates answers.

3. Final Answer:

   Tokenization + intent recognition + response generation -> Option A

4. Quick Check:

   Chatbot basics = Tokenize + Intent + Response [OK]

Hint: Chatbots need understanding + intent + reply steps [OK]

Common Mistakes:

• Confusing speech tasks with text understanding
• Choosing unrelated NLP tasks like summarization
• Mixing image tasks with NLP