N-grams in NLP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code generating bigrams?

Given the code below that generates bigrams from a sentence, what is the output?

NLP

sentence = "I love machine learning"
words = sentence.split()
bigrams = [(words[i], words[i+1]) for i in range(len(words)-1)]
print(bigrams)

A [('I', 'love'), ('love', 'machine'), ('learning', 'machine')]

B [('I', 'love'), ('love', 'machine'), ('machine', 'learning')]

C [('love', 'I'), ('machine', 'love'), ('learning', 'machine')]

D [('I', 'machine'), ('love', 'learning')]

💡 Hint

Remember bigrams are pairs of consecutive words.

The code splits the sentence into words and pairs each word with the next one, producing consecutive word pairs (bigrams).

🧠 Conceptual

intermediate

Which statement best describes the purpose of n-grams in text processing?

Choose the best description of why n-grams are used in natural language processing.

A They capture sequences of words to understand context and word order.

B They remove stop words to reduce noise in text data.

C They translate text from one language to another.

D They count the total number of characters in a text.

💡 Hint

Think about how n-grams help capture word relationships.

N-grams group words into sequences to help models learn context and order, which is important for understanding meaning.

❓ Hyperparameter

advanced

Choosing the right n for n-grams

If you want to capture longer phrases but avoid very sparse data, which n-gram size is usually the best choice?

A Unigrams (n=1) because they are simple and cover all words.

B Four-grams (n=4) because they capture the most detailed phrases.

C Bigrams (n=2) because they balance context and data sparsity.

D Trigrams (n=3) because longer sequences always improve accuracy.

💡 Hint

Longer n-grams capture more context but can cause data sparsity.

Bigrams often provide a good balance between capturing context and keeping data manageable without too much sparsity.

❓ Metrics

advanced

Evaluating n-gram language models

Which metric is commonly used to evaluate the quality of an n-gram language model?

A Perplexity, which measures how well the model predicts a sample.

B Accuracy, which counts correct word predictions only.

C Mean Squared Error, used for regression tasks.

D F1 Score, used for classification balance.

💡 Hint

This metric measures uncertainty in predicting text sequences.

Perplexity measures how surprised a model is by the test data; lower perplexity means better prediction.

🔧 Debug

expert

Why does this n-gram code raise an error?

Consider this code snippet to generate trigrams. Why does it raise an IndexError?

NLP

sentence = "Data science is fun"
words = sentence.split()
trigrams = [(words[i], words[i+1], words[i+2]) for i in range(len(words)-2)]
print(trigrams)

A The split method does not create a list of words.

B The print statement is missing parentheses.

C Tuples cannot have three elements in Python.

D The range goes too far, causing words[i+2] to exceed list length.

💡 Hint

Check the range limit for accessing words[i+2].

The loop tries to access words[i+2] even when i is near the end, causing an IndexError.

Practice

1. What is an n-gram in natural language processing?

easy

A. A random selection of n words from a text

B. A single word repeated n times

C. A sentence with n words

D. A group of n consecutive words in a text

Solution

1. Step 1: Understand the definition of n-gram

   An n-gram is defined as a sequence of n consecutive words appearing together in text.

2. Step 2: Compare options with definition

   Only A group of n consecutive words in a text correctly describes an n-gram as consecutive words, not random or repeated words.

3. Final Answer:

   A group of n consecutive words in a text -> Option D

4. Quick Check:

   n-gram = consecutive words [OK]

Hint: Remember: n-gram means consecutive words, not random ones [OK]

Common Mistakes:

• Thinking n-gram means repeated words
• Confusing n-gram with sentence length
• Assuming words are randomly picked

2. Which of the following is the correct way to set up a CountVectorizer to extract bigrams in Python?

easy

A. CountVectorizer(ngram_range=(1,1))

B. CountVectorizer(ngram_range=(2,2))

C. CountVectorizer(ngram_range=(0,2))

D. CountVectorizer(ngram_range=(1,3))

Solution

1. Step 1: Understand ngram_range parameter

   ngram_range=(2,2) extracts only bigrams (groups of exactly 2 words).

2. Step 2: Evaluate each option

   CountVectorizer(ngram_range=(1,1)) extracts unigrams only; C is invalid because 0 is not a valid n; D extracts unigrams to trigrams.

3. Final Answer:

   CountVectorizer(ngram_range=(2,2)) -> Option B

4. Quick Check:

   bigrams = ngram_range (2,2) [OK]

Hint: Set ngram_range=(2,2) for only bigrams [OK]

Common Mistakes:

• Using (1,1) which extracts unigrams
• Using (0,2) which is invalid
• Using (1,3) which extracts multiple n-grams

3. What will be the output tokens when extracting trigrams from the sentence 'I love machine learning' using CountVectorizer(ngram_range=(3,3))?

medium

A. ['I love machine', 'love machine learning']

B. ['I love', 'love machine', 'machine learning']

C. ['I', 'love', 'machine', 'learning']

D. ['I love machine learning']

Solution

1. Step 1: Understand trigram extraction

   Trigrams are groups of 3 consecutive words. The sentence has 4 words, so possible trigrams are words 1-3 and 2-4.

2. Step 2: List trigrams from the sentence

   First trigram: 'I love machine', second trigram: 'love machine learning'.

3. Final Answer:

   ['I love machine', 'love machine learning'] -> Option A

4. Quick Check:

   Trigrams = groups of 3 words [OK]

Hint: Count groups of 3 consecutive words for trigrams [OK]

Common Mistakes:

• Listing bigrams instead of trigrams
• Listing single words instead of groups
• Combining all words as one token

4. Identify the error in this code snippet for extracting bigrams:

from sklearn.feature_extraction.text import CountVectorizer
text = ['hello world']
vectorizer = CountVectorizer(ngram_range=(1,2))
vectorizer.fit_transform(text)
print(vectorizer.get_feature_names())

medium

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

B. The ngram_range should be (2,2) to extract only bigrams

C. The method get_feature_names() is deprecated and should be get_feature_names_out()

D. CountVectorizer cannot extract bigrams

Solution

1. Step 1: Check method usage

   In recent sklearn versions, get_feature_names() is deprecated; get_feature_names_out() is the correct method.

2. Step 2: Validate other parts

   ngram_range=(1,2) is valid for unigrams and bigrams; text as list is correct; CountVectorizer supports bigrams.

3. Final Answer:

   get_feature_names() is deprecated and should be get_feature_names_out() -> Option C

4. Quick Check:

   Use get_feature_names_out() for features [OK]

Hint: Use get_feature_names_out() instead of deprecated get_feature_names() [OK]

Common Mistakes:

• Thinking ngram_range=(1,2) is wrong for bigrams
• Assuming text must be a string, not list
• Believing CountVectorizer can't extract bigrams

5. You want to build a text prediction model that uses both unigrams and bigrams but excludes any n-grams containing stop words like 'the' or 'and'. Which approach is best?

hard

A. Use CountVectorizer with ngram_range=(1,2) and stop_words='english'

B. Use CountVectorizer with ngram_range=(2,2) and no stop words removal

C. Use CountVectorizer with ngram_range=(1,1) and manually remove stop words after extraction

D. Use CountVectorizer with ngram_range=(1,3) and stop_words=None

Solution

1. Step 1: Understand requirements

   We need unigrams and bigrams, and want to exclude stop words in any n-gram.

2. Step 2: Evaluate options

   Use CountVectorizer with ngram_range=(1,2) and stop_words='english' uses ngram_range=(1,2) for unigrams and bigrams and removes stop words automatically. Others either miss unigrams, include stop words, or include trigrams.

3. Final Answer:

   Use CountVectorizer with ngram_range=(1,2) and stop_words='english' -> Option A

4. Quick Check:

   Unigrams + bigrams + stop word removal = Use CountVectorizer with ngram_range=(1,2) and stop_words='english' [OK]

Hint: Set ngram_range and stop_words='english' to filter stop words [OK]

Common Mistakes:

• Not removing stop words from bigrams
• Using wrong ngram_range missing unigrams
• Including trigrams when not needed