Tokenization (word and sentence) in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is tokenization in Natural Language Processing?

Tokenization is the process of breaking down text into smaller pieces called tokens, which can be words, sentences, or subwords. It helps computers understand and analyze text.

beginner

What is the difference between word tokenization and sentence tokenization?

Word tokenization splits text into individual words, while sentence tokenization splits text into sentences. Both help organize text for easier processing.

intermediate

Why is tokenization important before training an NLP model?

Tokenization converts raw text into manageable pieces so models can learn patterns. Without tokenization, models can't understand the structure of language.

beginner

Example: Tokenize the sentence 'Hello world! How are you?' into words.

The word tokens are: ['Hello', 'world', '!', 'How', 'are', 'you', '?']

intermediate

What challenges can arise during tokenization?

Challenges include handling punctuation, contractions (like "don't"), abbreviations, and languages without spaces. Good tokenization handles these well.

What does sentence tokenization do?

A Removes stopwords

B Splits text into words

C Splits text into sentences

D Converts text to lowercase

Sentence tokenization breaks text into sentences, helping analyze text at the sentence level.

Which of these is a word token from the sentence 'I can't go'?

A can't

B cant

C ca n't

D can

The word token 'can't' keeps the contraction intact in many tokenization methods.

Why do we tokenize text before feeding it to an NLP model?

A To convert text into smaller, understandable pieces

B To translate text into another language

C To remove all punctuation

D To increase text length

Tokenization breaks text into tokens so models can process and learn from the text.

Which punctuation is usually treated as a separate token in word tokenization?

A Space

B Comma

C Letter

D Number

Punctuation like commas, periods, and question marks are often separate tokens.

Which is NOT a common challenge in tokenization?

A Handling contractions

B Handling languages without spaces

C Handling abbreviations

D Handling spaces in English

English uses spaces to separate words, so handling spaces is straightforward compared to other challenges.

Explain what tokenization is and why it is important in NLP.

Think about how computers read text and why smaller pieces help.

Describe the difference between word tokenization and sentence tokenization with examples.

Consider how you would split 'Hello world! How are you?'

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