Data extraction from text in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is data extraction from text?

Data extraction from text means pulling out useful information from written words, like names, dates, or places, so computers can understand and use it.

beginner

Name a common method used for extracting data from text.

One common method is called Named Entity Recognition (NER), which finds and labels things like people, places, and dates in text.

beginner

Why is data extraction from text important in real life?

It helps turn messy text into clear facts, like finding customer names in emails or dates in reports, making work faster and easier.

intermediate

What role does machine learning play in data extraction from text?

Machine learning teaches computers to spot patterns in text so they can find important information automatically without being told every time.

beginner

Give an example of a simple data extraction task.

Extracting all email addresses from a list of customer messages is a simple data extraction task.

What does Named Entity Recognition (NER) do?

A Finds and labels names, places, and dates in text

B Translates text into another language

C Counts the number of words in a text

D Removes punctuation from text

NER identifies and tags important entities like names and dates in text.

Which of these is NOT a typical use of data extraction from text?

A Finding customer names in emails

B Extracting dates from reports

C Pulling phone numbers from messages

D Drawing pictures from text

Drawing pictures is not related to extracting data from text.

Why use machine learning for data extraction?

A To delete all text data

B To make text harder to read

C To teach computers to find patterns and extract info automatically

D To print text on paper

Machine learning helps computers learn how to extract information without manual rules.

Which is an example of data extraction from text?

A Taking a photo

B Extracting email addresses from messages

C Listening to music

D Writing a story

Extracting emails is pulling useful info from text, which is data extraction.

What kind of information can data extraction from text find?

A Names, dates, places

B Colors of clothes

C Sounds in music

D Shapes of objects

Data extraction focuses on text info like names, dates, and places.

Explain in your own words what data extraction from text means and why it is useful.

Think about how you find important details in a long message.

Describe how machine learning helps computers extract data from text automatically.

Imagine teaching a friend to spot names in a story without telling them every time.

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