Modeling languages from graph networks

TL;DR

This paper introduces a novel approach to modeling languages by leveraging graph networks, set partitions, and Young tableaux to compute probability distributions of words, with applications across multiple fields.

Contribution

It presents a new mathematical framework combining graph theory and combinatorics to analyze language structures and word probabilities.

Findings

Provides a method to compute word probability distributions using graph networks.

Connects language modeling with advanced combinatorial and graph theoretical tools.

Applicable to diverse fields like bioinformatics and data mining.

Abstract

We model and compute the probability distribution of the letters in random generated words in a language by using the theory of set partitions, Young tableaux and graph theoretical representation methods. This has been of interest for several application areas such as network systems, bioinformatics, internet search, data mining and computacional linguistics.