On Bi-gram Graph Attributes

TL;DR

This paper introduces a bi-gram graph representation for text and corpus analysis, demonstrating its computational efficiency, versatility, and scalability for various semantic and corpus-level insights.

Contribution

It presents a novel bi-gram graph method for text analysis, highlighting its computational simplicity and broad applicability for large datasets.

Findings

Bi-gram graphs are computationally cheap to create.

The approach provides unique insights through graph attributes.

Scalable to large datasets with diverse use-cases.

Abstract

We propose a new approach to text semantic analysis and general corpus analysis using, as termed in this article, a "bi-gram graph" representation of a corpus. The different attributes derived from graph theory are measured and analyzed as unique insights or against other corpus graphs. We observe a vast domain of tools and algorithms that can be developed on top of the graph representation; creating such a graph proves to be computationally cheap, and much of the heavy lifting is achieved via basic graph calculations. Furthermore, we showcase the different use-cases for the bi-gram graphs and how scalable it proves to be when dealing with large datasets.