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']}

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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.