Domain Heuristic Fusion of Multi-Word Embeddings for Nutrient Value Prediction

Gordana Ispirova, Tome Eftimov and Barbara Koroušić Seljak
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Gordana Ispirova: Computer Systems Department, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Tome Eftimov: Computer Systems Department, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
Barbara Koroušić Seljak: Computer Systems Department, Jožef Stefan Institute, 1000 Ljubljana, Slovenia

Mathematics, 2021, vol. 9, issue 16, 1-15

Abstract: Being both a poison and a cure for many lifestyle and non-communicable diseases, food is inscribing itself into the prime focus of precise medicine. The monitoring of few groups of nutrients is crucial for some patients, and methods for easing their calculations are emerging. Our proposed machine learning pipeline deals with nutrient prediction based on learned vector representations on short text–recipe names. In this study, we explored how the prediction results change when, instead of using the vector representations of the recipe description, we use the embeddings of the list of ingredients. The nutrient content of one food depends on its ingredients; therefore, the text of the ingredients contains more relevant information. We define a domain-specific heuristic for merging the embeddings of the ingredients, which combines the quantities of each ingredient in order to use them as features in machine learning models for nutrient prediction. The results from the experiments indicate that the prediction results improve when using the domain-specific heuristic. The prediction models for protein prediction were highly effective, with accuracies up to 97.98%. Implementing a domain-specific heuristic for combining multi-word embeddings yields better results than using conventional merging heuristics, with up to 60% more accuracy in some cases.

Keywords: domain-specific embeddings; domain knowledge; machine learning; data mining; macronutrient prediction; representation learning; word embeddings; paragraph embeddings (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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