1. Introduction

In Natural Language Processing (NLP), one of the fundamental challenges is understanding the relationship between words in a sentence. Simple models like Bag of Words (BoW) treat every word independently and ignore how words are positioned or related to each other. For example, in the sentences:

• “The dog bit the man.”
• “The man bit the dog.”

Both sentences contain the same words, but their meanings are entirely different. The Bag of Words model would treat them as identical because it ignores word order.
This limitation motivated the development of N-Gram models, which preserve a limited amount of sequential information.

An N-Gram is a sequence of N consecutive items (usually words or characters) from a given text. By analyzing these sequences, we can understand local context and patterns in the text. N-Grams are used extensively in various NLP tasks such as text prediction, speech recognition, machine translation, and sentiment analysis.

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2. Intuitive Understanding of N-Grams

To grasp the concept of N-Grams, consider the sentence:

“I love learning NLP.”

Let’s see how different N values change the representation:

Type	Example	What It Captures
Unigram (1-gram)	[“I”, “love”, “learning”, “NLP”]	Treats each word as independent. Captures word frequency, not context.
Bigram (2-gram)	[(“I”, “love”), (“love”, “learning”), (“learning”, “NLP”)]	Captures short-term dependencies and immediate word relationships.
Trigram (3-gram)	[(“I”, “love”, “learning”), (“love”, “learning”, “NLP”)]	Captures broader context and phrase-level information.

So, N controls how much context the model sees at a time.

• For N = 1, it behaves like Bag of Words.
• For N = 2, it starts to understand relationships between pairs of words.
• For N = 3 or more, it captures even richer context but at the cost of higher computational complexity.

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3. Why Use N-Grams

Understanding how words occur together helps in numerous NLP applications. Below are the main motivations:

1. Preserves Local Context
   N-Grams keep track of the order in which words appear, allowing models to understand short-range dependencies like “New York” or “machine learning”.
2. Improves Predictive Power
   Many text-based models use previous N−1 words to predict the next one. For instance, if you type “I am going to”, the model can predict “school” or “work”.
3. Foundation for Language Models
   N-Grams form the basis of statistical language models, where probabilities of word sequences are calculated to measure how natural a sentence is.
4. Useful in Text Classification
   In sentiment analysis or spam detection, N-Grams can capture meaningful patterns like “not good”, “very happy”, or “free offer”.
5. Helps in Information Retrieval
   Improves keyword matching accuracy in search engines, where phrases like “artificial intelligence” are treated as a single concept.

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4. Types of N-Grams

N-Grams can be classified based on the number of tokens they include:

1. Unigrams (N=1):
   Represents individual words.
   Example: “The sky is blue.” → [“The”, “sky”, “is”, “blue”]
   Useful for simple models and feature extraction when context is not critical.
2. Bigrams (N=2):
   Represents pairs of consecutive words.
   Example: [(“The”, “sky”), (“sky”, “is”), (“is”, “blue”)]
   Captures direct relationships between neighboring words.
3. Trigrams (N=3):
   Represents triplets of consecutive words.
   Example: [(“The”, “sky”, “is”), (“sky”, “is”, “blue”)]
   Captures slightly longer context.
4. Higher-order N-Grams (N ≥ 4):
   Captures long-range dependencies (e.g., “once upon a time in”).
   However, they are rarely used in practice due to data sparsity and high dimensionality.

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5. Implementation Using NLTK

Now, let’s implement N-Grams using Python’s Natural Language Toolkit (NLTK) library.

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Step 1: Import Required Libraries

import nltk
from nltk import ngrams
from nltk.tokenize import word_tokenize

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Step 2: Download Required Resources

nltk.download('punkt')

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Step 3: Sample Text

text = "Natural Language Processing makes computers understand human language."

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Step 4: Tokenize the Text

Tokenization splits a sentence into individual words (tokens).

tokens = word_tokenize(text.lower())
print(tokens)

Output:

['natural', 'language', 'processing', 'makes', 'computers', 'understand', 'human', 'language', '.']

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Step 5: Generate N-Grams

Using nltk.ngrams():

unigrams = list(ngrams(tokens, 1))
bigrams = list(ngrams(tokens, 2))
trigrams = list(ngrams(tokens, 3))

print("Unigrams:", unigrams)
print("Bigrams:", bigrams)
print("Trigrams:", trigrams)

Output:

Unigrams: [('natural',), ('language',), ('processing',), ('makes',), ('computers',), ('understand',), ('human',), ('language',), ('.',)]
Bigrams: [('natural', 'language'), ('language', 'processing'), ('processing', 'makes'), ('makes', 'computers'), ('computers', 'understand'), ('understand', 'human'), ('human', 'language'), ('language', '.')]
Trigrams: [('natural', 'language', 'processing'), ('language', 'processing', 'makes'), ('processing', 'makes', 'computers'), ('makes', 'computers', 'understand'), ('computers', 'understand', 'human'), ('understand', 'human', 'language'), ('human', 'language', '.')]

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Step 6: Using Padding to Include Sentence Boundaries

Padding allows the model to consider start and end tokens:

padded = list(ngrams(tokens, 2, pad_left=True, pad_right=True, left_pad_symbol='<s>', right_pad_symbol='</s>'))
print(padded)

Output:

[('<s>', 'natural'), ('natural', 'language'), ('language', 'processing'), ('processing', 'makes'),
('makes', 'computers'), ('computers', 'understand'), ('understand', 'human'), ('human', 'language'), ('language', '</s>')]

This helps in language modeling, where the model needs to know how sentences start and end.

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Step 7: Count Frequency of N-Grams

from nltk import FreqDist

bigram_freq = FreqDist(bigrams)
print(bigram_freq.most_common(5))

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6. Applications of N-Grams

1. Text Prediction and Autocomplete
   When you type on your phone, predictive text models use N-Grams to suggest the next word based on previous context.
   Example: After typing “New”, the system might suggest “York”.
2. Speech Recognition
   N-Gram probabilities help select the most likely sequence of words when interpreting sounds.
3. Machine Translation
   In systems like Google Translate, N-Grams help maintain fluent sentence structure in the target language.
4. Sentiment Analysis
   N-Grams can detect context-specific phrases such as “not bad” or “very disappointed”, which single words cannot capture.
5. Search and Information Retrieval
   Search engines use N-Grams to understand multi-word queries like “artificial intelligence applications”.
6. Plagiarism Detection and Text Similarity
   N-Gram overlap between two documents helps measure textual similarity.

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7. Detailed Advantages of N-Grams

1. Captures Local Context

Unlike Bag of Words, which ignores sequence, N-Grams retain the order of nearby words. This makes them valuable for detecting patterns like “New York” or “not good”.

2. Simple and Efficient to Implement

N-Grams are conceptually straightforward and computationally lightweight for small N values (1–3). They can be implemented quickly using libraries like NLTK.

3. Effective for Predictive Models

In text prediction or autocomplete, N-Grams form the foundation of Markov-based language models, which rely on the probability of the next word given the previous ones.

4. Good Feature Extraction Technique

In text classification, N-Grams can improve performance by providing more discriminative features. For example, the bigram “very bad” carries a negative sentiment that “bad” alone cannot express.

5. Works with Both Words and Characters

You can apply N-Gram modeling at the character level as well, which helps in spelling correction, name entity extraction, and language identification.

6. Useful for Text Matching

N-Grams can measure similarity between two strings or documents by comparing overlapping sequences.

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8. Detailed Disadvantages of N-Grams

1. Data Sparsity Problem

As N increases, the number of possible combinations grows exponentially. Most of these combinations do not appear frequently, leading to sparse and inefficient data representations.

2. High Dimensionality

For large corpora with rich vocabularies, the feature space becomes huge. This makes storage and computation expensive, especially for trigrams and higher.

3. Limited Long-Term Context

Even though bigrams and trigrams capture short dependencies, they fail to understand long-range relationships such as connections between subjects and verbs across clauses.

4. Sensitivity to Noise and Tokenization

Minor changes like misspellings or punctuation differences can create new, unrelated N-Grams, reducing robustness.

5. Requires Large Datasets

Accurately estimating N-Gram probabilities requires vast amounts of data. Without sufficient text, the model may fail to generalize to unseen phrases.

6. Lack of Semantic Understanding

N-Grams rely purely on surface-level word sequences. They cannot capture meaning, sarcasm, or nuances that modern contextual models like BERT or GPT can.

7. Language and Domain Dependency

N-Grams built on one domain (e.g., movie reviews) might not work well in another (e.g., medical text), as word co-occurrence patterns differ.

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9. Summary Table

Aspect	N-Gram Strength	N-Gram Limitation
Context	Retains short-term context	Cannot capture long-range context
Complexity	Simple and interpretable	Becomes computationally heavy for large N
Semantics	Captures common phrases	No semantic understanding
Data Need	Works on moderate data	High-order N-Grams need large data
Applications	Useful in prediction, classification, translation	Poor generalization on unseen data

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10. Conclusion

The N-Gram model is a cornerstone concept in NLP that bridges the gap between simple frequency-based models (like Bag of Words) and complex contextual models (like Word2Vec or Transformers). It provides a structured way to capture word order and context while remaining simple and interpretable.

However, with its increasing dimensionality and sparsity challenges, N-Gram models are often replaced by more advanced deep learning approaches in modern NLP.
Still, they remain an excellent foundation for understanding how computers learn language patterns and are invaluable for classical NLP applications like text classification and autocomplete.