A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes

Morales-Garzón, Andrea; Gómez‐Romero, Juan; Martín-Bautista, María J.

doi:10.1109/access.2021.3058559

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…A domain-specific word embedding approach with a fuzzy metric that focuses on a unique entity recognition task is proposed to adopt cooking recipes from a set of all available recipes. The model achieves 95% confidence in selecting appropriate recipes (Morales-Garzón et al 2021 ). For the Chinese clinical NER task, the LSTM, CRF, and BERT models obtain an accuracy of 91.60% for EHR categorization.…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

“… Deng et al ( 2019 ) Named entity recognition SemEval 2013–2016 dataset LSTM Domain-specific word embedding The SSALSTM approach achieves an accuracy of 84.32% 16. Morales-Garzón et al ( 2021 ) Word embedding to understand ingredients relations to adopt food recipes Food.com dataset Unsupervised approach Word2Vec(CBOW), fastText, GloVe Word embedding with fuzzy metrics achieves 95% confidence in selecting appropriate food recipes 17. Yilmaz and Toklu ( 2020 ) Question classification task on Turkish question dataset Turkish question dataset CNN, LSTM, SVM Word2Vec (CBOW and Skip-Gram) CNN + Skip-Gram achieves an accuracy of 94% 18.…”

Section: Appendix Amentioning

confidence: 99%

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Impact of word embedding models on text analytics in deep learning environment: a review

2023

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The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

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Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

Section: Appendix Amentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

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“…In the last two decades, several researches have employed data mining strategies for identifying relevant patterns and useful information from food datasets [2]. Morales-Garzón et al [3] proposed an unsupervised algorithm for adapting ingredient recipes to user needs and preferences through a method based on word embeddings. As well, Matej Petkovi'c et al [4] designed a AI workflow methodology named Di-etHub to annotate and classify recipes with the food concepts related to them, divided into representation learning and two predictive modeling tasks, classification and hierarchical multi-label classification.…”

Section: Introductionmentioning

confidence: 99%

Food Data Analysis using Multidimensional Visualizations based on Point Placement

Holanda

Romão

Botelho

et al. 2022

Anais Estendidos Do XXXV Conference on Graphics, Patterns and Images (SIBGRAPI Estendido 2022)

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Food data comprise records regarding nutrients, ingredients, amounts of different vitamins and minerals that can be found in foods. The wide variety of food products that can be stored in large datasets makes the traditional analysis tasks unfeasible and time-consuming when conducted manually by the dietitians and related professionals. This paper describes a method for visualizing food data using point placement strategies to support specialists in tasks related to determining similar food products that can be replaced in specific diets. The proposed method generates a structured representation for food data to be used as input to some state-of-the-art and recent visualizations, such as PCA, t-SNE, UMAP and TriMap. Experiments were conducted to assess the quality of visualizations and the results reported that the nonlinear visualizations presented satisfactory discriminability regarding some food categories and better preservation of the data patterns. A case study based on a visual exploration process was also conducted and demonstrates the specialist successfully finding substitute food products for planning a vegan diet plan.

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