Entity types (PERSON, ORG, LOC, DATE) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

NLP

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Apple was founded by Steve Jobs in California.')
for ent in doc.[1]:
    print(ent.text, ent.label_)

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

Aspans

Bentities

Ctokens

Dents

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'entities' instead of 'ents' causes an attribute error.

The doc.ents attribute contains the named entities detected in the text.

2fill in blank

medium

Complete the code to filter and print only entities of type PERSON.

NLP

for ent in doc.ents:
    if ent.label_ == '[1]':
        print(ent.text)

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

ALOC

BDATE

CPERSON

DORG

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ORG' or 'LOC' will filter organizations or locations instead.

The entity label PERSON identifies people in the text.

3fill in blank

hard

Fix the error in the code to correctly count the number of DATE entities.

NLP

date_count = 0
for ent in doc.ents:
    if ent.label_ == '[1]':
        date_count += [2]
print('Number of dates:', date_count)

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

ADATE

BPERSON

C1

Dent

Attempts:

3 left

💡 Hint

Common Mistakes

Adding the entity object instead of 1 causes a TypeError.

We check if the entity label is 'DATE' and add 1 to the count for each match.

4fill in blank

hard

Fill both blanks to create a dictionary mapping entity text to their types for ORG and LOC entities.

NLP

entity_dict = {ent.[1]: ent.[2] for ent in doc.ents if ent.label_ in ['ORG', 'LOC']}

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

Atext

Blabel_

Cstart_char

Dend_char

Attempts:

3 left

💡 Hint

Common Mistakes

Using character positions instead of text or label causes wrong dictionary keys or values.

Use ent.text for the entity string and ent.label_ for its type.

5fill in blank

hard

Fill both blanks to print all unique entity types found in the text.

NLP

unique_labels = set(ent.[1] for ent in doc.[2])
for label in unique_labels:
    print(label)

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

Alabel_

Bents

D()

Attempts:

3 left

💡 Hint

Common Mistakes

Adding parentheses after label causes a TypeError in Python 3.

We get entity labels with ent.label_, iterate over doc.ents, and print labels directly.

Practice

1. Which entity type label would you use to mark the name "Albert Einstein" in a text?

easy

A. PERSON

B. ORG

C. LOC

D. DATE

Solution

1. Step 1: Understand entity types

   PERSON labels identify names of people in text.

2. Step 2: Match the example to entity type

   "Albert Einstein" is a person's name, so it fits PERSON.

3. Final Answer:

   PERSON -> Option A

4. Quick Check:

   PERSON = Albert Einstein [OK]

Hint: Names of people are always PERSON entities [OK]

Common Mistakes:

• Confusing ORG with PERSON
• Labeling locations as PERSON
• Using DATE for names

2. Which of the following is the correct way to label the entity type for "Google" in a named entity recognition task?

easy

A. LOC

B. ORG

C. PERSON

D. DATE

Solution

1. Step 1: Identify what Google represents

   Google is a company, which is an organization.

2. Step 2: Match to entity type

   ORG is the label for organizations like companies.

3. Final Answer:

   ORG -> Option B

4. Quick Check:

   ORG = Google [OK]

Hint: Companies and institutions are labeled ORG [OK]

Common Mistakes:

• Labeling companies as LOC
• Using PERSON for organizations
• Confusing DATE with ORG

3. Given the sentence: "Barack Obama visited Paris on July 14, 2015." Which of the following is the correct sequence of entity types for [Barack Obama, Paris, July 14, 2015]?

medium

A. [PERSON, LOC, ORG]

B. [ORG, LOC, DATE]

C. [PERSON, LOC, DATE]

D. [PERSON, ORG, DATE]

Solution

1. Step 1: Identify each entity type

   "Barack Obama" is a person, "Paris" is a location, and "July 14, 2015" is a date.

2. Step 2: Match entities to types in order

   The sequence is PERSON, LOC, DATE.

3. Final Answer:

   [PERSON, LOC, DATE] -> Option C

4. Quick Check:

   PERSON, LOC, DATE = Barack Obama, Paris, July 14, 2015 [OK]

Hint: Match each entity to person, place, or date in order [OK]

Common Mistakes:

• Confusing ORG with LOC
• Mixing DATE with ORG
• Wrong order of entity types

4. You have a named entity recognition model that labels "Amazon" as a LOC (location). What is the most likely error in this labeling?

medium

A. Amazon is an organization, so it should be ORG

B. Amazon is a person, so LOC is wrong

C. Amazon is a date, so LOC is incorrect

D. Amazon is a location, so LOC is correct

Solution

1. Step 1: Understand the entity "Amazon"

   Amazon is commonly known as a company (organization), not a location.

2. Step 2: Correct entity type for Amazon

   ORG is the correct label for companies like Amazon.

3. Final Answer:

   Amazon is an organization, so it should be ORG -> Option A

4. Quick Check:

   ORG = Amazon company [OK]

Hint: Companies are ORG, not LOC [OK]

Common Mistakes:

• Assuming Amazon is only a location
• Labeling company names as PERSON
• Ignoring context of entity

5. You want to extract all dates and locations from the sentence: "The conference was held in New York on March 3rd, 2023, and attended by experts from Google." Which entity types should your model identify to get the correct information?

hard

A. PERSON and LOC

B. PERSON and ORG

C. ORG and DATE

D. LOC and DATE

Solution

1. Step 1: Identify entities to extract

   The task is to extract dates and locations only.

2. Step 2: Match entity types for locations and dates

   Locations are labeled LOC and dates are labeled DATE.

3. Final Answer:

   LOC and DATE -> Option D

4. Quick Check:

   LOC and DATE = New York, March 3rd, 2023 [OK]

Hint: Dates = DATE, places = LOC [OK]

Common Mistakes:

• Extracting PERSON or ORG instead
• Mixing LOC with ORG
• Ignoring DATE entities