Named Entity Recognition basics in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the NER model from spaCy.

ML Python

import spacy
nlp = spacy.load('[1]')

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Aner_model

Ben_core_web_sm

Ctext_classifier

Dtokenizer

Attempts:

3 left

💡 Hint

Common Mistakes

Using a model name that does not exist in spaCy.

Confusing NER model with tokenizer or text classifier.

The correct spaCy model for basic English NER is en_core_web_sm.

2fill in blank

medium

Complete the code to extract named entities from the text.

ML Python

doc = nlp(text)
entities = [(ent.text, ent.[1]) for ent in doc.ents]

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Acategory

Btype

Ctag

Dlabel_

Attempts:

3 left

💡 Hint

Common Mistakes

Using ent.type which does not exist.

Using ent.tag which is for part-of-speech tags.

In spaCy, the entity label is accessed with ent.label_.

3fill in blank

hard

Fix the error in the code to correctly print entity text and label.

ML Python

for ent in doc.ents:
    print(ent.text, ent.[1])

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Aentity

Blabel

Clabel_

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using ent.label which returns an integer code, not the string.

Trying to print ent.entity which does not exist.

The correct attribute to get the entity label as a string is label_, not label.

4fill in blank

hard

Fill both blanks to create a dictionary of entity texts and their labels.

ML Python

entity_dict = {ent.[1]: ent.[2] for ent in doc.ents}

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Atext

Blabel_

Cstart_char

Dend_char

Attempts:

3 left

💡 Hint

Common Mistakes

Using character offsets instead of text or label.

Mixing up keys and values.

Use ent.text as the key and ent.label_ as the value to map entity text to its label.

5fill in blank

hard

Fill all three blanks to filter entities with label 'PERSON' and get their texts.

ML Python

person_entities = [ent.[1] for ent in doc.ents if ent.[2] == '[3]']

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Atext

Blabel_

CPERSON

Dent

Attempts:

3 left

💡 Hint

Common Mistakes

Using ent.label instead of ent.label_.

Comparing label to lowercase 'person' instead of uppercase 'PERSON'.

We want the text of entities where the label is 'PERSON'. So use ent.text, check ent.label_, and compare to 'PERSON'.