Primary Level – Trigram Model
The algorithm first attempts prediction using trigrams when sufficient contextual information is available.
This report documents the exploratory stage of developing a Context-Aware Next-Word Prediction Engine.
The primary objective of this project is to design an algorithm capable of learning linguistic patterns from large-scale text datasets and predicting the most probable next word a user may type.
The project focuses on analyzing textual relationships, understanding word frequency distributions, and building efficient n-gram prediction models suitable for deployment in a Shiny application.
data <- data.frame(
Source = c("Customer Support", "Documentation", "Journaling"),
Line_Count = c("~1.8M", "~950K", "~720K"),
Word_Count = c("~28M", "~31M", "~35M"),
Characteristics = c(
"Short, task-oriented",
"Structured, repetitive",
"Narrative, expressive"
)
)
datatable(
data,
options = list(pageLength = 5),
rownames = FALSE
)The datasets contain millions of words collected from multiple text sources. Each source contributes unique linguistic patterns that improve the overall prediction capability of the model.
plot_ly(
x = c("Customer Support", "Documentation", "Journaling"),
y = c(28, 31, 35),
type = "bar"
)The visualization above compares the approximate word counts across the datasets and highlights the variation in language structures and content volume.
A small group of highly frequent functional words dominates the datasets. This behavior follows common natural language distribution patterns.
Two-word and three-word combinations (bigrams and trigrams) provide strong contextual information for predicting the next word accurately.
Removing extremely rare words can significantly reduce model size while preserving prediction performance and runtime efficiency.
The algorithm first attempts prediction using trigrams when sufficient contextual information is available.
If no trigram match exists, the system backs off to bigram prediction.
When contextual matches are unavailable, the algorithm suggests the most common unigram predictions.
This back-off mechanism improves prediction coverage and helps handle unseen word combinations effectively.
The final Shiny application aims to provide:
This milestone confirms that the datasets were successfully processed and explored through statistical analysis and visualization techniques.
The exploratory analysis revealed meaningful linguistic patterns and validated the effectiveness of n-gram based prediction methods.
The implemented back-off strategy provides a strong foundation for building the final next-word prediction engine and deploying it within a scalable Shiny application.