Tokenization (word and sentence) in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to split the text into words using Python's split method.

NLP

text = "Hello world! Let's learn tokenization."
words = text.[1]()

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

Astrip

Bjoin

Creplace

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using join() instead of split()

Using replace() which changes characters

Using strip() which removes spaces only at ends

The split() method divides a string into a list of words based on spaces by default.

2fill in blank

medium

Complete the code to split the text into sentences using the nltk library.

NLP

import nltk
nltk.download('punkt')
text = "Hello world! Let's learn tokenization."
sentences = nltk.tokenize.[1](text)

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

Asent_tokenize

Bword_tokenize

Ctokenize_words

Dsplit

Attempts:

3 left

💡 Hint

Common Mistakes

Using word_tokenize which splits into words

Using split which is a string method, not nltk function

sent_tokenize splits text into sentences, while word_tokenize splits into words.

3fill in blank

hard

Fix the error in the code to tokenize words using nltk correctly.

NLP

import nltk
nltk.download('punkt')
text = "Hello world! Let's learn tokenization."
words = nltk.tokenize.word_tokenize([1])

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

Anltk

Bwords

Ctext

Dtokenize

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the wrong variable like 'words' which is not defined yet

Passing the module name instead of the text

The word_tokenize function needs the text string as input to split it into words.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, only for words longer than 3 characters.

NLP

text = "Tokenization splits text into words and sentences."
words = text.split()
lengths = { [1] : len([2]) for [1] in words if len([1]) > 3 }

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

Aword

Bwords

Ctext

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'words' instead of 'word' inside len()

Using 'text' which is the full string, not a single word

We use 'word' as the variable for each item in words, and len(word) gives the length.

5fill in blank

hard

Fill all three blanks to create a list of sentences from text, then a list of words from the first sentence, and finally count the words.

NLP

import nltk
nltk.download('punkt')
text = "Hello world! Let's learn tokenization."
sentences = nltk.tokenize.[1](text)
first_sentence_words = nltk.tokenize.word_tokenize([2])
word_count = len([3])

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

Asent_tokenize

Bsentences[0]

Cfirst_sentence_words

Dword_tokenize

Attempts:

3 left

💡 Hint

Common Mistakes

Using word_tokenize instead of sent_tokenize for sentences

Passing text instead of first sentence to word_tokenize

Counting length of sentences instead of words

sent_tokenize splits text into sentences; sentences[0] is the first sentence; first_sentence_words holds the tokenized words to count.

Practice

1. What is the main purpose of tokenization in natural language processing?

easy

A. To remove stop words from text

B. To translate text into another language

C. To split text into smaller units like words or sentences

D. To generate new sentences from text

Solution

1. Step 1: Understand tokenization

   Tokenization means breaking text into smaller pieces such as words or sentences.

2. Step 2: Identify the main goal

   The main goal is to prepare text for further processing by splitting it into tokens.

3. Final Answer:

   To split text into smaller units like words or sentences -> Option C

4. Quick Check:

   Tokenization = splitting text [OK]

Hint: Tokenization means cutting text into pieces [OK]

Common Mistakes:

• Confusing tokenization with translation
• Thinking tokenization removes words
• Believing tokenization generates new text

2. Which of the following Python code snippets correctly tokenizes a sentence into words using NLTK?

easy

A. from nltk.tokenize import word_tokenize
sentence = 'Hello world!'
tokens = word_tokenize(sentence)

B. import nltk
sentence = 'Hello world!'
tokens = nltk.split(sentence)

C. from nltk.tokenize import sent_tokenize
sentence = 'Hello world!'
tokens = sent_tokenize(sentence)

D. sentence = 'Hello world!'
tokens = sentence.split_words()

Solution

1. Step 1: Check correct import and function

   The correct function to tokenize words in NLTK is word_tokenize from nltk.tokenize.

2. Step 2: Verify code correctness

   from nltk.tokenize import word_tokenize sentence = 'Hello world!' tokens = word_tokenize(sentence) imports word_tokenize and applies it correctly to the sentence.

3. Final Answer:

   from nltk.tokenize import word_tokenize\nsentence = 'Hello world!'\ntokens = word_tokenize(sentence) -> Option A

4. Quick Check:

   Use word_tokenize for word splitting [OK]

Hint: Use word_tokenize from nltk.tokenize for words [OK]

Common Mistakes:

• Using sent_tokenize for word tokenization
• Calling non-existent split_words() method
• Using nltk.split which does not exist

3. What will be the output of this Python code using NLTK?

from nltk.tokenize import sent_tokenize
text = 'Hello world! How are you?'
sentences = sent_tokenize(text)
print(sentences)

medium

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

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

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

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

Solution

1. Step 1: Understand sent_tokenize function

   sent_tokenize splits text into sentences based on punctuation.

2. Step 2: Apply sent_tokenize to the text

   The text has two sentences: 'Hello world!' and 'How are you?'.

3. Final Answer:

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

4. Quick Check:

   sent_tokenize splits sentences correctly [OK]

Hint: sent_tokenize splits text at sentence ends [OK]

Common Mistakes:

• Confusing sent_tokenize with word_tokenize output
• Expecting no split for multiple sentences
• Ignoring punctuation as sentence boundary

4. Identify the error in this code snippet for word tokenization using NLTK:

import nltk
tokens = nltk.word_tokenize('Hello world!')

medium

A. The string should be a list, not a plain string

B. word_tokenize should be called as nltk.tokenize.word_tokenize

C. word_tokenize does not exist in NLTK

D. Missing import of word_tokenize from nltk.tokenize

Solution

1. Step 1: Check how word_tokenize is imported

   word_tokenize is in nltk.tokenize, not directly in nltk module.

2. Step 2: Identify correct import

   Must import word_tokenize specifically: from nltk.tokenize import word_tokenize.

3. Final Answer:

   Missing import of word_tokenize from nltk.tokenize -> Option D

4. Quick Check:

   Import word_tokenize correctly [OK]

Hint: Import word_tokenize from nltk.tokenize, not nltk [OK]

Common Mistakes:

• Assuming nltk.word_tokenize exists
• Trying to call word_tokenize without import
• Passing list instead of string to tokenizer

5. Given a paragraph with multiple sentences, how can you tokenize it into words while preserving sentence boundaries using NLTK?

hard

A. Use word_tokenize directly on the whole paragraph

B. Use sent_tokenize to split sentences, then word_tokenize each sentence separately

C. Use split() method on the paragraph string

D. Use sent_tokenize only, it also splits words

Solution

1. Step 1: Understand the need to preserve sentence boundaries

   Preserving sentence boundaries means keeping words grouped by sentences.

2. Step 2: Apply sent_tokenize then word_tokenize

   First split paragraph into sentences, then tokenize words in each sentence separately.

3. Final Answer:

   Use sent_tokenize to split sentences, then word_tokenize each sentence separately -> Option B

4. Quick Check:

   Split sentences first, then words [OK]

Hint: Split sentences first, then tokenize words inside each [OK]

Common Mistakes:

• Tokenizing words directly loses sentence grouping
• Using split() which is too simple
• Assuming sent_tokenize splits words