External knowledge transfer deployment inside a simple double agent Viterbi algorithm

TL;DR

This paper explores integrating external knowledge transfer into a double agent Viterbi algorithm to improve ingredient state estimation for unknown words, aiming to enhance performance in text-based ingredient extraction.

Contribution

It introduces a novel approach of applying external knowledge transfer directly to the state matrix calculation within a double agent Viterbi algorithm.

Findings

External knowledge transfer improves unknown word handling

Direct application on state matrix enhances accuracy

Method outperforms previous models in ingredient extraction

Abstract

We consider in this paper deploying external knowledge transfer inside a simple double agent Viterbi algorithm which is an algorithm firstly introduced by the author in his preprint "Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text". The key challenge of this work lies in discovering the reason why our old model does have bad performances when it is confronted with estimating ingredient state for unknown words and see if deploying external knowledge transfer directly on calculating state matrix could be the solution instead of deploying it only on back propagating step.