N-Gram Models
Analyzing text patterns.
N-Gram Models is a free Learn AI with Python lesson on CoddyKit — lesson 3 of 5. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Learn AI with Python learning path, one of 5 lessons in the course, and your progress syncs across the web and the CoddyKit app.
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N-Gram Models
N-Gram models are a fundamental concept in NLP. They analyze sequences of words or characters to identify patterns and relationships within text.
An N-Gram is a contiguous sequence of N items (words or characters) from a given text.
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What Are N-Grams?
N-Grams are defined by the value of N:
- Unigram (N=1): Single words (e.g., "I", "like", "NLP").
- Bigram (N=2): Pairs of words (e.g., "I like", "like NLP").
- Trigram (N=3): Triplets of words (e.g., "I like NLP").
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Generating N-Grams in Python
We can use Python to generate N-Grams from a text:
from nltk import ngrams
# Example text
text = "I love natural language processing"
# Tokenize text
tokens = text.split()
# Generate bigrams
bigrams = list(ngrams(tokens, 2))
print("Bigrams:", bigrams)
# Generate trigrams
trigrams = list(ngrams(tokens, 3))
print("Trigrams:", trigrams)4
Applications of N-Gram Models
N-Gram models are used in:
- Language Modeling: Predicting the next word in a sequence.
- Text Generation: Creating coherent sentences or paragraphs.
- Spelling Correction: Identifying probable word sequences.
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Frequency Analysis with N-Grams
We can analyze the frequency of N-Grams to identify common patterns in text:
from collections import Counter
# Example text
text = "I love NLP. NLP is amazing. I love learning NLP."
# Tokenize and generate bigrams
tokens = text.split()
bigrams = list(ngrams(tokens, 2))
# Count bigram frequency
bigram_freq = Counter(bigrams)
print("Bigram Frequencies:", bigram_freq.most_common())6
Advantages of N-Gram Models
N-Gram models have several advantages:
- Simple and easy to implement.
- Useful for basic text pattern analysis.
- Provide a foundation for more advanced NLP models.
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Limitations of N-Gram Models
Despite their usefulness, N-Gram models have some limitations:
- Struggle with long-range dependencies in text.
- Require large amounts of data for high N values.
- Generate repetitive or incoherent text for large sequences.
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Real-World Use Cases of N-Grams
N-Grams are used in:
- Autocomplete: Predicting the next word in search engines.
- Plagiarism Detection: Identifying overlapping word patterns.
- Speech Recognition: Recognizing common word sequences.
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Summary and Next Steps
In this lesson, we:
- Introduced N-Gram models and their types.
- Generated N-Grams in Python and analyzed their frequencies.
- Discussed the advantages, limitations, and applications of N-Gram models.
Next, we’ll explore sentiment analysis and learn how to determine the sentiment of text data.
Frequently asked questions
Is the “N-Gram Models” lesson free?
Yes — the full text of “N-Gram Models” is free to read here on the web, and the Learn AI with Python course includes 5 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Learn AI with Python course, upgrade to CoddyKit PRO.
What will I learn in “N-Gram Models”?
Analyzing text patterns. You practise Learn AI with Python with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start Learn AI with Python?
No prior experience is required. Learn AI with Python on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 5, so you can start here or from the beginning and move at your own pace.
How long does the “N-Gram Models” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this Learn AI with Python lesson?
Yes. Every Learn AI with Python lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.