Tokenization in spaCy in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of spaCy Tokenization Code

What is the output of the following code snippet that uses spaCy to tokenize a sentence?

NLP

import spacy
nlp = spacy.blank('en')
doc = nlp('Hello world! How are you?')
tokens = [token.text for token in doc]
print(tokens)

A ['Hello', 'world', '!', 'How', 'are', 'you', '?']

B ['Hello world!', 'How are you?']

C ['Hello', 'world!', 'How', 'are', 'you?']

D ['Hello', 'world', 'How', 'are', 'you']

💡 Hint

Think about how spaCy splits punctuation from words by default.

spaCy tokenizes text by separating punctuation from words, so 'world!' becomes 'world' and '!' as separate tokens.

🧠 Conceptual

intermediate

spaCy Tokenizer Behavior on Contractions

Which statement correctly describes how spaCy tokenizes contractions like "don't" by default?

A It removes the apostrophe and returns 'dont' as one token.

B It keeps "don't" as a single token.

C It splits "don't" into three tokens: 'do', 'n', and 't'.

D It splits "don't" into two tokens: 'do' and 'n't'.

💡 Hint

Think about how spaCy handles common English contractions.

spaCy splits contractions into base word and contraction part, so "don't" becomes 'do' and 'n't'.

❓ Hyperparameter

advanced

Changing spaCy Tokenizer Behavior

Which spaCy component or method would you customize to change how tokens are split, for example to keep 'New York' as one token?

A Modify the tokenizer exceptions or add special cases to the tokenizer.

B Change the pipeline's tagger component settings.

C Adjust the parser's dependency rules.

D Modify the lemmatizer's dictionary.

💡 Hint

Token splitting is controlled before tagging or parsing.

The tokenizer controls how text is split into tokens; adding special cases can keep multi-word expressions as one token.

❓ Metrics

advanced

Evaluating Tokenization Accuracy

You have a gold standard tokenization and a spaCy tokenizer output. Which metric best measures how well spaCy tokenized the text compared to the gold standard?

A Perplexity of the tokenizer output.

B Token-level F1 score comparing spaCy tokens to gold tokens.

C Sentence-level BLEU score.

D Accuracy of part-of-speech tags.

💡 Hint

Think about comparing sets of tokens for overlap.

Token-level F1 score measures precision and recall of tokens compared to gold standard, suitable for tokenization evaluation.

🔧 Debug

expert

Identifying Tokenization Bug in spaCy Customization

You added a special case to spaCy's tokenizer to keep 'San Francisco' as one token, but after running, it still splits into two tokens. What is the most likely cause?

NLP

import spacy
from spacy.symbols import ORTH

nlp = spacy.blank('en')
special_case = [{ORTH: 'San Francisco'}]
nlp.tokenizer.add_special_case('San Francisco', special_case)
doc = nlp('I visited San Francisco last year.')
tokens = [token.text for token in doc]
print(tokens)

A The ORTH symbol is incorrect; it should be LEMMA.

B The tokenizer needs to be rebuilt after adding special cases.

C The special case should be a list of dicts with separate tokens, not a single dict with the full phrase.

D The blank model 'en' does not support special cases.

💡 Hint

Check the format of the special case argument.

Special cases require a list of token dicts, each with ORTH for each token; a single dict with full phrase is invalid.

Practice

1. What does tokenization do in spaCy?

easy

A. It splits text into smaller pieces called tokens.

B. It trains a machine learning model.

C. It translates text into another language.

D. It visualizes text data.

Solution

1. Step 1: Understand tokenization concept

   Tokenization means breaking text into smaller parts called tokens, like words or punctuation.

2. Step 2: Relate to spaCy functionality

   spaCy uses tokenization to prepare text for analysis by splitting it into tokens.

3. Final Answer:

   It splits text into smaller pieces called tokens. -> Option A

4. Quick Check:

   Tokenization = splitting text [OK]

Hint: Tokenization means breaking text into tokens [OK]

Common Mistakes:

• Confusing tokenization with training models
• Thinking tokenization translates text
• Assuming tokenization visualizes data

2. Which of the following is the correct way to load the English model in spaCy for tokenization?

easy

A. import spacy; nlp = spacy.tokenize('en')

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

C. import spacy; nlp = spacy.model('english')

D. import spacy; nlp = spacy.load_model('english')

Solution

1. Step 1: Recall spaCy model loading syntax

   spaCy loads models using spacy.load with the model name as a string.

2. Step 2: Identify correct model name and function

   The English small model is 'en_core_web_sm' and loaded by spacy.load('en_core_web_sm').

3. Final Answer:

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

4. Quick Check:

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

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

Common Mistakes:

• Using spacy.tokenize instead of spacy.load
• Wrong model names like 'english' instead of 'en_core_web_sm'
• Using non-existent functions like load_model

3. What will be the output tokens list from this code snippet?

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Hello, world!')
tokens = [token.text for token in doc]
print(tokens)

medium

A. ['Hello', ',', 'world', '!']

B. ['Hello,', 'world!']

C. ['Hello world']

D. ['Hello', 'world!']

Solution

1. Step 1: Understand spaCy tokenization behavior

   spaCy splits punctuation from words, so commas and exclamation marks become separate tokens.

2. Step 2: Analyze the given text 'Hello, world!'

   Tokens will be 'Hello', ',', 'world', and '!' separately.

3. Final Answer:

   ['Hello', ',', 'world', '!'] -> Option A

4. Quick Check:

   spaCy separates punctuation as tokens [OK]

Hint: Remember spaCy splits punctuation into separate tokens [OK]

Common Mistakes:

• Keeping punctuation attached to words
• Combining words into one token
• Ignoring punctuation tokens

4. Identify the error in this spaCy tokenization code:

import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp('Test sentence.')
for token in doc:
print(token.text)

medium

A. The token.text attribute does not exist.

B. Wrong model name used in spacy.load.

C. Missing indentation for print inside the for loop.

D. The variable 'doc' is not defined.

Solution

1. Step 1: Check Python syntax for loops

   Python requires the code inside a for loop to be indented properly.

2. Step 2: Inspect the given code

   The print statement is not indented under the for loop, causing an IndentationError.

3. Final Answer:

   Missing indentation for print inside the for loop. -> Option C

4. Quick Check:

   Indent loop body code in Python [OK]

Hint: Indent code inside loops to avoid errors [OK]

Common Mistakes:

• Ignoring Python indentation rules
• Assuming model name is wrong
• Thinking token.text is invalid

5. You want to tokenize a sentence but keep contractions like "don't" as one token using spaCy. Which approach is best?

hard

A. Use the default spaCy tokenizer without changes.

B. Split contractions manually after tokenization.

C. Replace contractions with full words before tokenization.

D. Modify the tokenizer exceptions to keep contractions as single tokens.

Solution

1. Step 1: Understand spaCy's default tokenizer behavior

   By default, spaCy splits contractions like "don't" into two tokens: 'do' and "n't".

2. Step 2: Identify how to keep contractions as one token

   Modifying tokenizer exceptions allows spaCy to treat contractions as single tokens.

3. Final Answer:

   Modify the tokenizer exceptions to keep contractions as single tokens. -> Option D

4. Quick Check:

   Customize tokenizer exceptions to control token splits [OK]

Hint: Change tokenizer exceptions to keep contractions whole [OK]

Common Mistakes:

• Using default tokenizer expecting contractions as one token
• Splitting contractions manually after tokenization
• Replacing contractions before tokenization unnecessarily