Next-Word Prediction Engine

Statistical N-Gram Language Modeling & Shiny Application

Data Product Team

2026-08-18

Executive Summary

  • Core Goal: Build an N-gram model to predict the next word using the preceding 1, 2, or 3 words.
  • Data Source: Model built by sampling and cleaning three textual datasets (Twitter, blogs, news).
  • Workflow: Reads and cleans source text \(\rightarrow\) builds predictive model \(\rightarrow\) validates via verification script.
  • Optimization: A pre-computation layer creates a fast lookup dictionary matching search phrases to target words.
  • Deployment: The complete analytics pipeline is packaged into an interactive Shiny web application.

Creation of N-Gram Tables

  • Sentence Tokenization: Built sentence-aware pipelines to preserve contextual boundaries.
  • Sequence Generation: Created overlapping sequences of 1, 2, 3, and 4 words (Unigrams to Quadgrams).
  • Frequency Filtering: Captured structural patterns from the text data to isolate high-probability sequences.
  • Target Mapping: Isolated the final word of each sequence into a distinct pre-calculated target column.

Prediction Model Architecture

  • Iterative Design: Executed and refined multiple modeling strategies to minimize prediction error.
  • Hierarchical Backoff: Implemented a statistical backoff model searching from 4-gram \(\rightarrow\) 3-gram \(\rightarrow\) 2-gram \(\rightarrow\) unigram.
  • Jelinek-Mercer Interpolation: Engineered a 4-gram model using modified JM interpolation.
  • Statistical Blending: Simultaneously blends quadgram, trigram, and bigram statistics for optimal probability distribution.

Shiny Application Deployment

  • Hosting Environment: The interactive solution is actively deployed and running on shinyapps.io.
  • Instant Response: Seamlessly loads a text input box that accepts real-time, open-ended user phrases.
  • Dynamic Fetching: Instantly displays the highest-frequency predictions directly from the lookup dictionary.
  • Reactive UI: Evaluates text inputs continuously to update predictions with minimal latency.

Evaluation & Model Constraints

  • Predictive Performance: Achieved a 50% accuracy score on entirely unobserved, open-ended test sentences.
  • Zipf’s Law: Rare phrases occur so infrequently that even a large 50% text sample fails to capture them.
  • Exact Match Limitation: Statistical N-gram models are limited because they require seeing exact text matches.
  • Data Sparsity: Quantified uncertainty highlights that unseen context requires smoothing or interpolation.