Hidden Markov Based Mathematical Model dedicated to Extract Ingredients from Recipe Text

TL;DR

This paper introduces a Hidden Markov Model-based approach for extracting ingredients from recipe texts, achieving higher accuracy than traditional methods by effectively handling unknown words.

Contribution

The work presents a novel Hidden Markov Model tailored for ingredient extraction in recipes, improving accuracy over existing POS tagging techniques.

Findings

Achieved high accuracy in ingredient extraction

Outperformed traditional POS tagging methods

Effectively handled unknown words in text

Abstract

Natural Language Processing (NLP) is a branch of artificial intelligence that gives machines the ability to decode human languages. Partof-speech tagging (POS tagging) is a pre-processing task that requires an annotated corpus. Rule-based and stochastic methods showed remarkable results for POS tag prediction. On this work, I performed a mathematical model based on Hidden Markov structures and I obtained a high-level accuracy of ingredients extracted from text recipe with performances greater than what traditional methods could make without unknown words consideration.