NLP applications in real world - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Which NLP application is best suited for automatically summarizing long documents?

Imagine you have a very long article and want a short summary that captures the main points. Which NLP application would you use?

A Text Summarization

B Machine Translation

C Named Entity Recognition (NER)

D Sentiment Analysis

💡 Hint

Think about which task reduces text length while keeping important info.

Text Summarization creates a shorter version of a long text, keeping key ideas. NER finds names, Sentiment Analysis finds feelings, and Machine Translation changes language.

❓ Predict Output

intermediate

What is the output of this sentiment analysis code snippet?

Given the code below that uses a simple rule-based sentiment check, what will be the printed output?

NLP

text = "I love sunny days but hate the rain."
positive_words = ['love', 'happy', 'joy']
negative_words = ['hate', 'sad', 'angry']
score = 0
for word in text.lower().split():
    if word in positive_words:
        score += 1
    elif word in negative_words:
        score -= 1
if score > 0:
    print('Positive')
elif score < 0:
    print('Negative')
else:
    print('Neutral')

A Negative

B Neutral

C Positive

D SyntaxError

💡 Hint

Count how many positive and negative words appear exactly in the list.

The text contains 'love' (positive) and 'hate' (negative), so score is 0, resulting in 'Neutral'.

❓ Model Choice

advanced

Which model type is most appropriate for real-time speech-to-text transcription?

You want to convert spoken words into text instantly during a live conversation. Which model type is best suited?

A Recurrent Neural Network (RNN) with attention

B Transformer-based sequence-to-sequence model

C K-Means clustering

D Convolutional Neural Network (CNN) for image classification

💡 Hint

Consider models that handle sequences and context well for language tasks.

Transformer-based seq2seq models are state-of-the-art for speech-to-text, handling long sequences efficiently. RNNs are older and slower, CNNs are for images, and K-Means is unsupervised clustering.

❓ Metrics

advanced

Which metric is most suitable to evaluate a machine translation system?

You built a system that translates English sentences to French. Which metric should you use to measure how good the translations are?

A Accuracy

B Mean Squared Error

C BLEU score

D F1 score

💡 Hint

Think about metrics that compare generated text to reference translations.

BLEU score measures how close the machine translation is to human translations by comparing overlapping words and phrases. Accuracy and F1 are for classification, MSE is for regression.

🔧 Debug

expert

What error does this code raise when running a named entity recognition (NER) pipeline?

Consider this Python code using a popular NLP library to extract named entities. What error will it raise?

NLP

from transformers import pipeline
ner = pipeline('ner')
text = "Apple is looking at buying U.K. startup for $1 billion"
results = ner(text, aggregation_strategy='simple')
print(results)

A TypeError: pipeline() got an unexpected keyword argument 'aggregation_strategy'

B ValueError: aggregation_strategy must be one of ['none', 'simple']

C RuntimeError: Model weights not found

D No error, prints list of entities

💡 Hint

Check if the 'aggregation_strategy' argument is valid for the current transformers version.

In recent versions of transformers, 'aggregation_strategy' is a valid argument for the NER pipeline and will return aggregated entities without error.

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