N-Gram Language Model with Smoothing (Streamlit App)

This project implements a Bigram (2-gram) Language Model using Natural Language Processing. It supports two smoothing techniques—Laplace Smoothing and Good-Turing Smoothing—to estimate probabilities for seen and unseen word pairs. The model is interactive and runs in a Streamlit web app.

🚀 Features

• 📁 Upload your own .txt corpus
• 🧠 Generate bigrams and count their frequencies
• 📊 Apply Laplace and Good-Turing smoothing
• 🔍 Enter custom bigrams to check their probabilities
• 📈 Compare probabilities side-by-side

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🛠️ Requirements

Install the required Python packages: pip install -r requirements.txt

▶️ How to Run

streamlit run app.py

📂 File Structure

ngram_language_model/ │ ├── app.py # Main Streamlit application ├── ngram_utils.py # Utility functions for preprocessing and probability calculation ├── requirements.txt # Python dependencies └── long_sample_corpus.txt# Example text corpus

📚 Smoothing Techniques

Laplace Smoothing: Adds 1 to every bigram count to avoid zero probabilities. Good-Turing Smoothing: Recalculates probability based on the frequency of frequency counts.

🧪 Example Test Bigrams

("language", "models") ("speech", "recognition") ("deep", "learning") (Unseen bigram)

🧠 Educational Goals

This project is great for: Understanding the mechanics of N-gram models Seeing the effects of smoothing Hands-on exploration of NLP probability models