Language Modeling & N-grams in NLP

Language Modeling is a fundamental concept in Natural Language Processing (NLP). It helps computers understand how likely a sentence is and predict the next word in a sequence.

What is a Language Model?

A Language Model (LM) calculates the probability of a sequence of words.

It helps machines:

  • Understand sentences
  • Predict next words

Examples of Language Model

  1. “I am going to school” ✔
  2. “I am going to moon” ❌
  3. “She is reading a book” ✔
  4. “She is reading a banana” ❌
  5. “They are playing cricket” ✔

Probability Concept

A sentence probability is calculated using conditional probability:

P(w1, w2, …, wn) = Product of probabilities

What are N-grams?

N-grams are sequences of words used to simplify language modeling.

Types of N-grams

  • Unigram → Single word
  • Bigram → Two-word sequence
  • Trigram → Three-word sequence

Examples of N-grams (5 Examples)

Example 1:

“I love NLP”

  • Bigram → I love, love NLP

Example 2:

“Machine learning is fun”

  • Bigram → Machine learning, learning is

Example 3:

“Deep learning models are powerful”

  • Trigram → Deep learning models

Example 4:

“I am going home”

  • Trigram → I am going

Example 5:

“Natural language processing is amazing”

  • Bigram → Natural language, language processing

Bigram Probability

P(word₂ | word₁) = Count(word₁, word₂) / Count(word₁)

Probability Examples

  1. P(love | I) = 1
  2. P(likes | He) = 1
  3. P(eats | She) = 1
  4. P(play | They) = 1
  5. P(watch | We) = 1

Applications of Language Models

  • Auto-complete systems
  • Chatbots
  • Speech recognition
  • Machine translation
  • Search engines

Limitations

  • Cannot understand long context
  • Data sparsity problem
  • High memory usage
  • Rare words get zero probability
  • Limited understanding

Conclusion

Language Modeling and N-grams are essential for predicting word sequences in NLP. They form the foundation for many modern AI applications like chatbots and translation systems.