NER with spaCy in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the English model in spaCy.

NLP

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

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

Aen_model

Benglish_core

Cspacy_en

Den_core_web_sm

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect model names like 'english_core' or 'en_model'.

Forgetting to install the model before loading.

The correct model name to load the small English model in spaCy is 'en_core_web_sm'.

2fill in blank

medium

Complete the code to process the text and create a spaCy Doc object.

NLP

doc = nlp('[1]')

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

Anlp

B"Apple is looking at buying a startup"

Ctext

DDoc

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the nlp object itself instead of a string.

Passing a variable name without quotes.

To create a Doc object, you pass the text string to the nlp model, e.g., nlp("Apple is looking at buying a startup").

3fill in blank

hard

Fix the error in the code to print named entities and their labels.

NLP

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

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

Aents

Blabels

Centities

Dtokens

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tokens' or 'labels' which are not attributes of Doc for entities.

Using 'entities' which is not a valid attribute.

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

4fill in blank

hard

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

NLP

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

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

Atext

Blabel_

Clabel

Dstring

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'label' without underscore which returns an integer code.

Using 'string' which is not an attribute.

Each entity has a 'text' attribute for the entity string and 'label_' attribute for the label name.

5fill in blank

hard

Fill all three blanks to filter entities with label 'ORG' and create a list of their texts.

NLP

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

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

Atext

Blabel_

CORG

Dlabel

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'label' instead of 'label_' which gives numeric codes.

Comparing label to 'org' in lowercase which is case sensitive.

To get texts of entities labeled 'ORG', use ent.text and check ent.label_ == 'ORG'.

Practice

1. What does NER (Named Entity Recognition) do in natural language processing?

easy

A. It generates new text based on input prompts.

B. It translates text from one language to another.

C. It summarizes long documents into short paragraphs.

D. It finds and labels important names and terms in text automatically.

Solution

1. Step 1: Understand NER's purpose

   NER identifies specific names like people, places, or organizations in text.

2. Step 2: Compare with other NLP tasks

   Translation, summarization, and text generation are different tasks than NER.

3. Final Answer:

   It finds and labels important names and terms in text automatically. -> Option D

4. Quick Check:

   NER = Finds names and terms [OK]

Hint: NER extracts names and terms, not translations or summaries [OK]

Common Mistakes:

• Confusing NER with translation or summarization
• Thinking NER generates new text
• Believing NER only finds keywords, not named entities

2. Which of the following is the correct way to load a pre-trained spaCy model for NER?

easy

A. import spacy; nlp = spacy.load('en_core_web_sm')

B. import spacy; nlp = spacy.model('en_core_web_sm')

C. import spacy; nlp = spacy.load_model('en_core_web_sm')

D. import spacy; nlp = spacy.get('en_core_web_sm')

Solution

1. Step 1: Recall spaCy model loading syntax

   spaCy uses spacy.load('model_name') to load pre-trained models.

2. Step 2: Check each option

   Only import spacy; nlp = spacy.load('en_core_web_sm') uses spacy.load correctly; others use invalid functions.

3. Final Answer:

   import spacy; nlp = spacy.load('en_core_web_sm') -> Option A

4. Quick Check:

   spaCy model loading = spacy.load() [OK]

Hint: Use spacy.load('model_name') to load models [OK]

Common Mistakes:

• Using spacy.model or spacy.load_model which don't exist
• Trying spacy.get which is not a spaCy function
• Forgetting to import spacy before loading

3. Given this code snippet using spaCy for NER:

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Apple is looking at buying U.K. startup for $1 billion')
entities = [(ent.text, ent.label_) for ent in doc.ents]
print(entities)

What will be the output?

medium

A. [('Apple', 'PERSON'), ('U.K.', 'ORG'), ('$1 billion', 'QUANTITY')]

B. [('Apple', 'ORG'), ('startup', 'ORG'), ('$1 billion', 'MONEY')]

C. [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')]

D. [('Apple', 'GPE'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')]

Solution

1. Step 1: Understand spaCy NER labels

   Apple is recognized as an organization (ORG), U.K. as geopolitical entity (GPE), and $1 billion as money (MONEY).

2. Step 2: Match entities with labels

   [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')] correctly matches these entities and labels as spaCy outputs.

3. Final Answer:

   [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')] -> Option C

4. Quick Check:

   spaCy NER output matches [('Apple', 'ORG'), ('U.K.', 'GPE'), ('$1 billion', 'MONEY')] [OK]

Hint: Check spaCy's common entity labels for correct matches [OK]

Common Mistakes:

• Confusing ORG with PERSON or GPE
• Mislabeling MONEY as QUANTITY
• Including words like 'startup' as entities

4. You run this code but get an error:

import spacy
doc = nlp('Google is a tech giant')

What is the most likely cause?

medium

A. spaCy does not support the word 'Google'.

B. The variable 'nlp' is not defined before use.

C. The text input is too short for NER.

D. Missing parentheses in the print statement.

Solution

1. Step 1: Check variable definitions

   The code uses 'nlp' without defining it by loading a spaCy model first.

2. Step 2: Identify error cause

   This causes a NameError because 'nlp' is undefined.

3. Final Answer:

   The variable 'nlp' is not defined before use. -> Option B

4. Quick Check:

   Undefined variable 'nlp' causes error [OK]

Hint: Always load model with spacy.load before using nlp [OK]

Common Mistakes:

• Assuming text length causes error
• Thinking spaCy can't recognize common words
• Confusing print syntax errors with variable errors

5. You want to extract only person names from a text using spaCy's NER. Which code snippet correctly filters for persons?

hard

A. persons = [ent.text for ent in doc.ents if ent.label_ == 'PERSON']

B. persons = [ent.text for ent in doc.ents if ent.label_ == 'ORG']

C. persons = [ent.text for ent in doc.ents if ent.label_ == 'GPE']

D. persons = [ent.text for ent in doc.ents if ent.label_ == 'MONEY']

Solution

1. Step 1: Identify label for persons in spaCy

   spaCy uses 'PERSON' label for people names.

2. Step 2: Filter entities by 'PERSON'

   Filtering doc.ents by ent.label_ == 'PERSON' extracts only person names.

3. Final Answer:

   persons = [ent.text for ent in doc.ents if ent.label_ == 'PERSON'] -> Option A

4. Quick Check:

   Filter entities by 'PERSON' label [OK]

Hint: Filter entities with label_ == 'PERSON' to get names [OK]

Common Mistakes:

• Using wrong labels like ORG or GPE for persons
• Not filtering entities at all
• Confusing entity text with label