Tokenization in spaCy in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the spaCy English model.

NLP

import spacy
nlp = spacy.[1]("en_core_web_sm")

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

Aload

Btokenize

Cprocess

Dparse

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tokenize' or 'parse' instead of 'load' to load the model.

The correct function to load a spaCy model is 'load'.

2fill in blank

medium

Complete the code to create a spaCy Doc object from text.

NLP

doc = nlp([1])

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

Anlp

Bsentence

Ctext

D"This is a sentence."

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a variable name without defining it.

Passing the nlp object itself.

You need to pass a string of text to the nlp object to create a Doc.

3fill in blank

hard

Fix the error in the code to print tokens from the Doc object.

NLP

for token in doc:
    print(token.[1])

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

Astring

Btokens

Ctext

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tokens' or 'word' which are not valid token attributes.

The attribute to get the token text is 'text'.

4fill in blank

hard

Fill both blanks to create a list of token texts for tokens longer than 3 characters.

NLP

tokens = [token.[1] for token in doc if len(token.[2]) > 3]

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

Atext

Blemma_

Dpos_

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lemma_' or 'pos_' which are not strings for length check.

We use 'token.text' to get the token string and check its length.

5fill in blank

hard

Fill all three blanks to create a dictionary of token texts and their part-of-speech tags for tokens longer than 4 characters.

NLP

token_pos = {token.[1]: token.[2] for token in doc if len(token.[3]) > 4}

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

Atext

Bpos_

Dlemma_

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'lemma_' instead of 'pos_' for POS tags.

Checking length on 'pos_' which is not a string.

We use 'text' for keys, 'pos_' for values, and 'text' to check length.

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