N-grams in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create bigrams from a list of words.

NLP

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

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

A1

B2

C3

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Using 2 instead of 1 causes index errors or skips words.

Using 0 pairs the same word with itself.

To create bigrams, we pair each word with the next one, so the index difference is 1.

2fill in blank

medium

Complete the code to generate trigrams from a list of words.

NLP

words = ['deep', 'learning', 'is', 'fun']
trigrams = [(words[i], words[i+1], words[i+[1]]) for i in range(len(words)-2)]
print(trigrams)

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

A3

B1

C0

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 or 3 causes incorrect grouping or index errors.

Using 0 pairs the same word multiple times.

For trigrams, the third word is two positions ahead of the first, so the index difference is 2.

3fill in blank

hard

Fix the error in the code to correctly generate bigrams.

NLP

words = ['AI', 'is', 'awesome']
bigrams = [(words[i], words[i+[1]]) for i in range(len(words)-1)]
print(bigrams)

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

A0

B2

C1

D-1

Attempts:

3 left

💡 Hint

Common Mistakes

Using range(len(words)) causes index error when accessing words[i+1].

Using index difference other than 1 breaks bigram logic.

The range should stop before the last index to avoid index error; the index difference is 1 for bigrams.

4fill in blank

hard

Fill both blanks to create a dictionary of word counts from a list.

NLP

words = ['data', 'science', 'data', 'AI']
word_counts = {word: words.count([1]) for word in [2]
print(word_counts)

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

Aword

Bwords

Cset(words)

Dlist(words)

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'words' instead of 'word' counts the whole list repeatedly.

Using 'words' instead of 'set(words)' causes duplicate keys.

We count each unique word, so we use 'word' for counting and 'set(words)' to get unique words.

5fill in blank

hard

Fill all three blanks to filter bigrams that start with 'machine'.

NLP

words = ['machine', 'learning', 'is', 'fun']
bigrams = [(words[i], words[i+[1]]) for i in range(len(words)-1)]
filtered = [bigram for bigram in bigrams if bigram[[2]] == [3]]
print(filtered)

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

A1

B0

C'machine'

D'learning'

Attempts:

3 left

💡 Hint

Common Mistakes

Using index 1 checks the second word instead of the first.

Using wrong index difference breaks bigram creation.

Filtering for 'learning' instead of 'machine' changes the result.

The index difference for bigrams is 1; to check the first word in the tuple, use index 0; filter for 'machine'.

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