SafeEat: Extraction of Information About the Presence of Food Allergens in Recipes

Amato, Alessandra; Cozzolino, Giovanni

doi:10.1007/978-3-030-57796-4_19

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Aiello and al (Aiello, Schifanella, Quercia, & Del Prete, 2019) discovered what are the most important predictors in food responsible of three diseases in a population situated in London. (Amato & Cozzolino, 2020) extracted ingredients from food text to alert readers from allergens presence in a recipe. I agree with (Van Erp et al, 2021) concerning how challenging to use IA in food domain and how it will resolve issues concerning the creation of a data driven analysis of nutrition.…”

Section: Previous Workmentioning

confidence: 99%

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

Baklouti¹

2019

Preprint

View full text Add to dashboard Cite

Natural Language Processing (NLP) is a branch of machine learning that gives the machines the ability to decode human languages. Part-of-speech tagging (POS tagging) is a preprocessing task that requires an annotated corpora. Rule-based and stochastic methods showed great 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 which is a performance greater than what traditional methods could make without unknown words consideration.

show abstract

Section: Previous Workmentioning

confidence: 99%

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

Baklouti¹

2019

Preprint

View full text Add to dashboard Cite

show abstract

“…However, making the information on food accessible is far from trivial. Analysis of digitized or digital recipes is a new and upcoming field of research, with publications linked to nutritional and health studies ( Reinivuo et al, 2009 ; Trattner et al, 2017 ), computational linguistics ( Jurafsky, 2015 ), computational gastronomy ( Jain et al, 2015 ), shopping ( Aiello et al, 2019 ), allergen detection ( Alemany-Bordera et al 2016 ; Amato and Cozzalino, 2020 ), and the Semantic Web ( Haussmann et al, 2019 ). Several research challenges are at the crossroads of data engineering, intelligent food, and cooking recipes, as discussed at the recent IEEE DECOR@ICDE workshop series ( Andres et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

Erp¹,

Reynolds²,

Maynard³

et al. 2021

Front. Artif. Intell.

View full text Add to dashboard Cite

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

show abstract

Processamento de Linguagem Natural na Identificação de Alérgenos em Rótulos Alimentares: Uma Aplicação no Contexto Brasileiro

Gadelha,

Pereira,

Guedes

et al. 2024

J Health Inform

View full text Add to dashboard Cite

Objetivo: Alergias alimentares impactam parte significativa da população, apresentando desafios para a saúde pública. A abordagem para o manejo dessas alergias exige a eliminação de alimentos que as desencadeiam. Entretanto, ler e interpretar rótulos de alimentos é desafiador devido à nomenclatura variada e inconsistente, bem como à falta de regulamentação adequada. Para o contexto brasileiro, propomos uma solução de Processamento de Linguagem Natural, que será integrada a um aplicativo móvel dedicado. Método: Para o reconhecimento das diversas nomenclaturas associadas aos quatro principais alérgenos, foi desenvolvido um banco de dados de alérgenos e um modelo de reconhecimento de entidades nomeadas, além de funções de pré-processamento de texto. Resultados: a avaliação dos modelos obteve uma precisão média de 96.50. Conclusão: Esta solução apoia a promoção de práticas alimentares mais seguras para indivíduos com alergias alimentares, fornecendo suporte tecnológico para obter informações sobre a presença de alérgenos em alimentos.

show abstract

SafeEat: Extraction of Information About the Presence of Food Allergens in Recipes

Cited by 3 publications

References 10 publications

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

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

Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

Processamento de Linguagem Natural na Identificação de Alérgenos em Rótulos Alimentares: Uma Aplicação no Contexto Brasileiro

Contact Info

Product

Resources

About