NER with spaCy in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Named Entity Recognition with spaCy

What is the output of the following code snippet that uses spaCy to detect named entities in a sentence?

NLP

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)

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

B [('Apple', 'PERSON'), ('U.K.', 'LOC'), ('$1 billion', 'MONEY')]

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

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

💡 Hint

Look at common entity labels spaCy uses for organizations, geopolitical entities, and money.

spaCy labels 'Apple' as an organization (ORG), 'U.K.' as a geopolitical entity (GPE), and '$1 billion' as money (MONEY).

❓ Model Choice

intermediate

Choosing the Correct spaCy Model for NER

Which spaCy model is best suited for performing Named Entity Recognition on English text with a balance of speed and accuracy?

A en_core_web_md

B en_vectors_web_lg

C en_core_web_lg

D en_core_web_sm

💡 Hint

Consider models that include trained pipelines for NER and balance size and speed.

en_core_web_md provides a good balance of speed and accuracy with medium-sized vectors and trained NER pipeline.

❓ Hyperparameter

advanced

Effect of Batch Size in spaCy NER Training

During training a spaCy NER model, what is the effect of increasing the batch size parameter?

A It slows down training and always improves model accuracy.

B It causes the model to overfit immediately.

C It has no effect on training speed or accuracy.

D It speeds up training but may reduce model accuracy due to less frequent weight updates.

💡 Hint

Think about how batch size affects gradient updates and training speed.

Larger batch sizes process more examples before updating weights, speeding training but possibly reducing accuracy due to less frequent updates.

❓ Metrics

advanced

Evaluating spaCy NER Model Performance

Which metric is most appropriate to evaluate the quality of a spaCy Named Entity Recognition model on a test dataset?

A Accuracy of token classification

B Mean Squared Error

C F1-score

D Perplexity

💡 Hint

NER is a sequence labeling task with imbalanced classes; consider precision and recall.

F1-score balances precision and recall, making it ideal for evaluating NER models where both false positives and false negatives matter.

🔧 Debug

expert

Debugging spaCy NER Model Training Error

You run spaCy NER training code but get this error: 'ValueError: [E088] The component 'ner' is not initialized. Call 'nlp.begin_training()' first.' Which code snippet fixes this error?

A

nlp = spacy.load('en_core_web_sm')
ner = nlp.get_pipe('ner')
optimizer = nlp.begin_training()

B

nlp = spacy.blank('en')
ner = nlp.add_pipe('ner')
optimizer = nlp.begin_training()

C

nlp = spacy.load('en_core_web_sm')
optimizer = nlp.create_optimizer()

D

nlp = spacy.blank('en')
optimizer = nlp.begin_training()

💡 Hint

The error means the 'ner' component is missing or not initialized properly.

When creating a blank model, you must add the 'ner' pipe before calling begin_training(). Option B does this correctly.

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