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

arXiv: https://arxiv.org/abs/2110.15707 · HTML: https://mlsanity.com/paper/2110.15707/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Food-bridging: a new network construction to unveil the principles of cooking](https://mlsanity.com/paper/1704.03330/) (2017)
- [Large-scale and high-resolution analysis of food purchases and health outcomes](https://mlsanity.com/paper/1905.00140/) (2019)
- [Kissing Cuisines: Exploring Worldwide Culinary Habits on the Web](https://mlsanity.com/paper/1610.08469/) (2016)

## Built on by (most cited first, 1 of 1)

- [Tweet Sentiment Extraction using Viterbi Algorithm with Transfer Learning](https://mlsanity.com/paper/2308.05973/) (2023)

## Abstract (arXiv)

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.