Data extraction from text in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the main purpose of Named Entity Recognition (NER) in data extraction?

Named Entity Recognition (NER) is a common technique in data extraction from text. What does NER primarily do?

A It identifies and classifies key information like names, dates, and locations in text.

B It translates text from one language to another.

C It summarizes long documents into short paragraphs.

D It generates new text based on input prompts.

💡 Hint

Think about what kind of information you want to pull out from a text document.

NER finds specific pieces of information such as names, dates, and places, which helps in extracting structured data from unstructured text.

❓ Predict Output

intermediate

Output of simple regex extraction code

What is the output of the following Python code that extracts all email addresses from a text?

Prompt Engineering / GenAI

import re
text = 'Contact us at [e-mail] or [e-mail].'
emails = re.findall(r'\b[\w.-]+@[\w.-]+\.\w+\b', text)
print(emails)

A ['[e-mail]', '[e-mail]']

B ['[e-mail] [e-mail]']

C ['support@example', 'sales@example']

D []

💡 Hint

Look at the regex pattern and what re.findall returns.

The regex matches email patterns and re.findall returns a list of all matches found in the text.

❓ Model Choice

advanced

Best model type for extracting structured data from unstructured text

You want to extract structured information like product names, prices, and dates from customer reviews. Which model type is best suited for this task?

A K-Means clustering for grouping similar texts

B Generative Adversarial Network (GAN) for data generation

C Convolutional Neural Network (CNN) for image classification

D Recurrent Neural Network (RNN) or Transformer-based model for sequence labeling

💡 Hint

Think about models that handle sequences and labeling tasks.

RNNs and Transformer models are designed to process sequences and can label each token, making them suitable for extracting structured data from text.

❓ Metrics

advanced

Evaluating extraction accuracy with precision and recall

You built a model to extract dates from text. On a test set, it found 80 dates, of which 60 were correct. The test set actually contains 100 dates. What are the precision and recall?

A Precision = 0.80, Recall = 0.60

B Precision = 0.60, Recall = 0.75

C Precision = 0.75, Recall = 0.60

D Precision = 0.60, Recall = 0.80

💡 Hint

Precision = correct found / total found; Recall = correct found / total actual.

Precision = 60/80 = 0.75; Recall = 60/100 = 0.60.

🔧 Debug

expert

Why does this extraction code raise an error?

Consider this Python code snippet for extracting phone numbers. Why does it raise an error?

Prompt Engineering / GenAI

import re
text = 'Call me at 123-456-7890 or 987-654-3210.'
pattern = r'\d{3}-\d{3}-\d{4}'
matches = re.match(pattern, text)
print(matches.group())

A The regex pattern is invalid and causes a SyntaxError.

B re.match only checks the start of the string, so it returns None causing an AttributeError.

C The text variable is empty, so no matches are found.

D matches.group() is called before re.match is imported.

💡 Hint

Check how re.match works compared to re.findall or re.search.

re.match tries to match the pattern only at the start of the string. Since the phone number is not at the start, it returns None, so calling group() causes an error.

Practice

1. What is the main goal of data extraction from text in AI?

easy

A. To find and pull out useful information like names and dates from text

B. To translate text from one language to another

C. To generate new text based on a prompt

D. To compress text files to save space

Solution

1. Step 1: Understand the purpose of data extraction

   Data extraction means finding specific useful info inside text, such as names, dates, or places.

2. Step 2: Compare options to the definition

   Only To find and pull out useful information like names and dates from text matches this purpose exactly, while others describe different tasks like translation or compression.

3. Final Answer:

   To find and pull out useful information like names and dates from text -> Option A

4. Quick Check:

   Data extraction = find useful info [OK]

Hint: Look for the option about finding info inside text [OK]

Common Mistakes:

• Confusing extraction with translation
• Thinking extraction means generating new text
• Mixing extraction with file compression

2. Which of the following is the correct way to call a function extract_entities with a text input doc in Python?

easy

A. extract_entities = doc()

B. extract_entities(doc)

C. extract_entities.doc()

D. extract_entities->doc()

Solution

1. Step 1: Recall Python function call syntax

   In Python, to call a function with an argument, use function_name(argument).

2. Step 2: Check each option

   extract_entities(doc) uses correct syntax: extract_entities(doc). Options A, C, and D are invalid Python syntax for calling a function.

3. Final Answer:

   extract_entities(doc) -> Option B

4. Quick Check:

   Function call = function_name(argument) [OK]

Hint: Remember Python calls use parentheses with arguments inside [OK]

Common Mistakes:

• Using dot notation to call a function
• Assigning function call to function name
• Using arrow notation like other languages

3. Given this Python code using a simple extraction model:

text = "Alice met Bob on 2023-04-01 in Paris."
entities = extract_entities(text)
print(entities)

If extract_entities returns a list of tuples with (entity, type), what is the expected output?

medium

A. {'Alice': 'PERSON', 'Bob': 'PERSON', '2023-04-01': 'DATE', 'Paris': 'LOCATION'}

B. ['Alice', 'Bob', '2023-04-01', 'Paris']

C. None

D. [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')]

Solution

1. Step 1: Understand the function output format

   The function returns a list of tuples, each tuple has (entity, type).

2. Step 2: Match output to expected format

   [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')] matches a list of tuples with entity and type pairs. ['Alice', 'Bob', '2023-04-01', 'Paris'] is just a list of strings, A is a dictionary, and D is None.

3. Final Answer:

   [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')] -> Option D

4. Quick Check:

   List of (entity, type) tuples = [('Alice', 'PERSON'), ('Bob', 'PERSON'), ('2023-04-01', 'DATE'), ('Paris', 'LOCATION')] [OK]

Hint: Look for list of tuples format with entity and type [OK]

Common Mistakes:

• Confusing list of strings with list of tuples
• Expecting dictionary instead of list
• Assuming function returns None

4. You have this code snippet:

def extract_entities(text):
    entities = []
    for word in text.split():
        if word.istitle():
            entities.append((word, 'PERSON'))
    return entities

text = "John and Mary went to London."
print(extract_entities(text))

What is the bug in this code for extracting entities?

medium

A. It only detects words starting with uppercase, missing multi-word names

B. It does not split text into words

C. It returns a string instead of a list

D. It crashes because of missing import

Solution

1. Step 1: Analyze the extraction logic

   The code checks if each word starts with uppercase (istitle) and labels it as 'PERSON'.

2. Step 2: Identify limitation

   This misses multi-word names like 'New York' or full names with multiple words. It only detects single capitalized words.

3. Final Answer:

   It only detects words starting with uppercase, missing multi-word names -> Option A

4. Quick Check:

   Single-word detection limitation = It only detects words starting with uppercase, missing multi-word names [OK]

Hint: Check if code handles multi-word names or just single words [OK]

Common Mistakes:

• Thinking split() is missing
• Assuming return type is wrong
• Expecting import needed for this code

5. You want to extract dates and locations from a large text using a pretrained AI model. Which approach best improves accuracy and speed?

hard

A. Use a generic language model without any fine-tuning

B. Manually write rules to find dates and locations using string matching

C. Use a named entity recognition (NER) model fine-tuned on your domain data

D. Extract all capitalized words as locations and all numbers as dates

Solution

1. Step 1: Consider model choice for extraction

   Fine-tuning a NER model on your specific domain helps it learn patterns and improves accuracy.

2. Step 2: Compare other options

   Manual rules are slow and brittle, generic models lack domain knowledge, and simple heuristics miss many cases.

3. Final Answer:

   Use a named entity recognition (NER) model fine-tuned on your domain data -> Option C

4. Quick Check:

   Fine-tuned NER model = best accuracy and speed [OK]

Hint: Fine-tune NER models for best extraction results [OK]

Common Mistakes:

• Relying on manual rules only
• Using generic models without tuning
• Using simple heuristics that miss cases