On Bi-gram Graph Attributes
This provides a new tool for researchers in natural language processing and corpus analysis, though it appears incremental as it builds on existing graph theory concepts.
The paper tackles text semantic and corpus analysis by introducing a bi-gram graph representation, showing it is computationally cheap and scalable for large datasets.
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.