N-gram Word Predictor App: Final Project

• Subtitle: A predictive text app powered by R and Shiny.
• Overview:
  • Predicts the next word in a phrase using pre-computed bigram and trigram frequency tables.
  • Designed for ease of use and efficient performance.
  • https://rpubs.com/joseph_bloomquist/1265455
  • https://josephbloomquist.shinyapps.io/text_prediction/
• Contact Information: Joseph_Bloomquist@outlook.com

How It Works

• N-gram Language Modeling:

  • Trigrams: Used for phrases with two or more words.
    • Matches the last two words against trigram data.
  • Bigrams: Used for single words.
    • Matches the last word against bigram data.

• Efficiency Optimizations:

  • Data stored as pre-computed frequency tables.
  • Queries use data.table for fast lookups.

• Description:
  • A simple interface with a text input for phrases and a ‘Predict Next Word’ button.
  • Displays the predicted word instantly.

Instructions

1. Enter 1–3 words in the input box.
2. Click the ‘Predict Next Word’ button.
3. View the predicted word in the output box.

Using the App

• Ease of Use:
  • Intuitive design with minimal input required.
  • Predictions are displayed almost instantly.
• Performance:
  • Efficient due to pre-computed n-gram frequency tables.
• Challenges:
  • Limited to 1–3 words as input.
  • No advanced context-based predictions or multi-language support.
• Strengths:
  • Leverages statistical modeling rather than complex neural networks for basic text prediction.

Why Hire Me?

• Skills Demonstrated:
  • Proficiency in R, Shiny, and data manipulation.
  • Ability to create deployment-ready apps with efficient memory management.
  • Strong understanding of NLP techniques.
• Hireability:

  • This project showcases the ability to deliver scalable, user-friendly solutions.

  • Demonstrates how to use pre-computed n-gram models for real-time text prediction.