N-grams in NLP - Model Pipeline Trace

Model Pipeline - N-grams

This pipeline shows how text data is transformed into N-grams, which are groups of consecutive words. These N-grams help the model understand word patterns to make predictions or analyze text.

Data Flow - 5 Stages

1Raw Text Input

1000 sentences x variable length→Collect raw sentences as input data→1000 sentences x variable length

"I love machine learning"

↓

2Text Cleaning

1000 sentences x variable length→Lowercase and remove punctuation→1000 sentences x variable length

"i love machine learning"

↓

3Tokenization

1000 sentences x variable length→Split sentences into words (tokens)→1000 sentences x variable length tokens

["i", "love", "machine", "learning"]

↓

4N-gram Generation (n=2)

1000 sentences x variable length tokens→Create pairs of consecutive words (bigrams)→1000 sentences x (variable length - 1) bigrams

["i love", "love machine", "machine learning"]

↓

5Vectorization

1000 sentences x variable length bigrams→Convert bigrams into numerical features (counts)→1000 rows x 5000 bigram features

Feature vector with counts of each bigram

Training Trace - Epoch by Epoch

Loss
1.0 |          *
0.8 |        *  
0.6 |      *    
0.4 |    *      
0.2 |  *        
0.0 +-----------
     1 2 3 4 5  Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.6	Model starts learning word patterns from bigrams
2	0.65	0.72	Loss decreases and accuracy improves as model learns
3	0.5	0.8	Model captures important bigram features
4	0.4	0.85	Training converges with good accuracy
5	0.35	0.88	Final epoch shows stable improvement

Prediction Trace - 6 Layers

Layer 1: Input Sentence

Layer 2: Text Cleaning

Layer 3: Tokenization

Layer 4: N-gram Generation (bigrams)

Layer 5: Vectorization

Layer 6: Model Prediction

Model Quiz - 3 Questions

What does the N in N-grams represent?

A Number of consecutive words grouped together

B Number of sentences processed

C Number of characters in a word

D Number of punctuation marks removed

Key Insight

N-grams help models understand word order and context by grouping words together. Converting these groups into numbers allows the model to learn patterns that improve predictions over time.

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