External knowledge transfer deployment inside a simple double agent Viterbi algorithm
This work addresses a domain-specific issue in natural language processing for recipe text analysis, but it appears incremental as it builds on a previously introduced model without broader implications.
The paper tackled the problem of poor performance in estimating ingredient states for unknown words in a Viterbi algorithm by deploying external knowledge transfer directly on the state matrix calculation, rather than only on the backpropagation step, resulting in improved accuracy as indicated by the abstract's focus on addressing this specific bottleneck.
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.