Information extraction patterns in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to extract named entities from text using spaCy.

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.[1]:
    print(ent.text, ent.label_)

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

Aentities

Bents

Ctokens

Dspans

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'entities' instead of 'ents' causes an AttributeError.

Trying to iterate over 'tokens' instead of entities.

The correct attribute to access named entities in a spaCy Doc object is 'ents'.

2fill in blank

medium

Complete the code to extract all email addresses from a text using a regular expression.

NLP

import re
text = 'Contact us at [e-mail] or [e-mail]'
emails = re.findall(r'[1]', text)
print(emails)

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

A\b\w+\b

B\d{3}-\d{2}-\d{4}

C[\w\.-]+@[\w\.-]+\.[a-zA-Z]{2,6}

Dhttps?://[\w\.-]+

Attempts:

3 left

💡 Hint

Common Mistakes

Using regex for phone numbers or URLs instead of emails.

Missing escape characters in regex.

The regex '[\w\.-]+@[\w\.-]+\.[a-zA-Z]{2,6}' matches typical email addresses.

3fill in blank

hard

Fix the error in the code to extract dates from text using spaCy's Matcher.

NLP

from spacy.matcher import Matcher
import spacy
nlp = spacy.load('en_core_web_sm')
matcher = Matcher(nlp.vocab)
pattern = [{'ENT_TYPE': '[1]'}]
matcher.add('DATE_PATTERN', [pattern])
doc = nlp('We met on January 10th, 2023.')
matches = matcher(doc)
for match_id, start, end in matches:
    span = doc[start:end]
    print(span.text)

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

ADATE

BTIME

CPERSON

DORG

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'TIME' or other entity types that do not match dates.

Not using the correct key 'ENT_TYPE' in the pattern.

The entity type for dates in spaCy is 'DATE'.

4fill in blank

hard

Fill both blanks to create a dictionary of word frequencies from a text.

NLP

text = 'apple banana apple orange banana apple'
words = text.split()
freq = {word: [1] for word in words if words.count(word) [2] 1}
print(freq)

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

Awords.count(word)

Bwords.index(word)

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using index instead of count for frequency.

Using '==' instead of '>' to filter words.

We count occurrences of each word and keep only those with count greater than 1.

5fill in blank

hard

Fill all three blanks to extract entities and their labels into a dictionary using spaCy.

NLP

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Google was founded in September 1998 by Larry Page and Sergey Brin.')
entities = [1]
for ent in doc.ents:
    entities[ent.[2]] = ent.[3]
print(entities)

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

A{}

Btext

Clabel_

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using list instead of dictionary for entities.

Using ent.label instead of ent.label_.

We create an empty dictionary {}, then use ent.text as key and ent.label_ as value.

Practice

1. What is the main purpose of information extraction patterns in NLP?

easy

A. To automatically find specific facts like names or dates in text

B. To translate text from one language to another

C. To generate new sentences from given words

D. To summarize long documents into short paragraphs

Solution

1. Step 1: Understand the role of information extraction patterns

   These patterns are designed to locate specific pieces of information such as names, dates, or places within text automatically.

2. Step 2: Compare with other NLP tasks

   Translation, generation, and summarization are different NLP tasks and do not focus on extracting facts.

3. Final Answer:

   To automatically find specific facts like names or dates in text -> Option A

4. Quick Check:

   Information extraction = find facts [OK]

Hint: Patterns extract facts, not translate or summarize [OK]

Common Mistakes:

• Confusing extraction with translation
• Thinking patterns generate new text
• Mixing extraction with summarization

2. Which of the following is a correct example of a simple pattern to extract dates in text?

easy

A. \b[A-Z]{2,}\b (matches uppercase words)

B. \b\d{4}-\d{2}-\d{2}\b (matches YYYY-MM-DD format)

C. \w+@\w+\.com (matches email addresses)

D. \d+\s+\w+ (matches any number followed by a word)

Solution

1. Step 1: Identify the pattern for dates

   The pattern \b\d{4}-\d{2}-\d{2}\b matches a 4-digit year, 2-digit month, and 2-digit day separated by dashes, which is a common date format.

2. Step 2: Check other options

   \d+\s+\w+ (matches any number followed by a word) matches number + word but is too general; C matches emails; A matches uppercase words, not dates.

3. Final Answer:

   \b\d{4}-\d{2}-\d{2}\b (matches YYYY-MM-DD format) -> Option B

4. Quick Check:

   Date pattern = \b\d{4}-\d{2}-\d{2}\b (matches YYYY-MM-DD format) [OK]

Hint: Look for year-month-day format in regex [OK]

Common Mistakes:

• Choosing patterns that match emails or words instead of dates
• Ignoring word boundaries \b in regex
• Confusing number patterns with date formats

3. Given this pattern to extract person names: \b(Mr|Ms|Dr)\.\s+[A-Z][a-z]+\b, what will be the output when applied to the text: "Dr. Smith and Mr. Johnson went to the park."?

medium

A. ["Dr", "Mr"]

B. ["Smith", "Johnson"]

C. ["Dr. Smith", "Mr. Johnson"]

D. [] (empty list)

Solution

1. Step 1: Understand the regex pattern

   The pattern matches titles (Mr, Ms, Dr) followed by a dot, a space, and a capitalized last name.

2. Step 2: Apply pattern to the text

   In the text, "Dr. Smith" and "Mr. Johnson" both match the pattern exactly.

3. Final Answer:

   ["Dr. Smith", "Mr. Johnson"] -> Option C

4. Quick Check:

   Pattern matches title + name = ["Dr. Smith", "Mr. Johnson"] [OK]

Hint: Match title + dot + space + capitalized name [OK]

Common Mistakes:

• Extracting only last names without titles
• Extracting only titles without names
• Getting empty results due to pattern mismatch

4. Identify the error in this pattern meant to extract email addresses: \b[\w.-]+@[\w.-]+\b

medium

A. It misses the domain extension like .com or .org

B. It uses incorrect character classes for emails

C. It does not match the '@' symbol

D. It matches only uppercase letters

Solution

1. Step 1: Analyze the pattern components

   The pattern matches word characters, dots, or dashes before and after '@', but stops at word boundary without requiring domain extensions like '.com'.

2. Step 2: Identify missing part

   Valid emails usually end with a domain extension (e.g., '.com'), which this pattern does not enforce, so it may match incomplete emails.

3. Final Answer:

   It misses the domain extension like .com or .org -> Option A

4. Quick Check:

   Email pattern missing domain extension = It misses the domain extension like .com or .org [OK]

Hint: Check if pattern includes domain extensions like .com [OK]

Common Mistakes:

• Assuming '@' is not matched
• Thinking character classes are wrong
• Ignoring domain extension importance

5. You want to extract locations from text using patterns that match city names followed by state abbreviations, like "Austin TX" or "Denver CO". Which pattern best fits this task?

hard

A. \b\w+@\w+\.com\b (email addresses)

B. \b\d{5}\b (five digit numbers)

C. \b[A-Z]{2,}\b (two or more uppercase letters only)

D. \b[A-Z][a-z]+\s+[A-Z]{2}\b (capitalized city name + space + two uppercase letters)

Solution

1. Step 1: Understand the location format

   Locations are city names starting with a capital letter followed by a two-letter uppercase state abbreviation.

2. Step 2: Match pattern to format

   Pattern \b[A-Z][a-z]+\s+[A-Z]{2}\b matches a capitalized word, a space, then exactly two uppercase letters, fitting the example.

3. Final Answer:

   \b[A-Z][a-z]+\s+[A-Z]{2}\b (capitalized city name + space + two uppercase letters) -> Option D

4. Quick Check:

   City + state abbreviation pattern = \b[A-Z][a-z]+\s+[A-Z]{2}\b (capitalized city name + space + two uppercase letters) [OK]

Hint: City capitalized + space + 2 uppercase letters [OK]

Common Mistakes:

• Choosing patterns for zip codes or emails
• Matching only uppercase words without city name
• Ignoring space between city and state