A Word Embedding Model for Mapping Food Composition Databases Using Fuzzy Logic

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

doi:10.1007/978-3-030-50143-3_50

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Consequently, food items need previous processing to handle them jointly. According to this idea, we can use the item textual description to identify equivalent items between databases, allowing the joint use of these databases as a unique data collection (Morales-Garzón et al, 2020). Notice that ontology-based methods could perform well in this problem.…”

Section: Heterogeneous Data-handlingmentioning

confidence: 99%

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Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain

Morales-Garzón

Gómez‐Romero

Martín-Bautista

2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research W

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Most works in food computing focus on generating new recipes from scratch. However, there is a large number of new online recipes generated daily with a large number of users reviews, with recommendations to improve the recipe flavor and ideas to modify them. This fact encourages the use of these data for obtaining improved and customized versions. In this thesis, we propose an adaptation engine based on fine-tuning a word embedding model. We will capture, in an unsupervised way, the semantic meaning of the recipe ingredients. We will use their word embedding representations to align them to external databases, thus enriching their data. The adaptation engine will use this food data to modify a recipe into another fitting specific user preferences (e.g., decrease caloric intake or make a recipe). We plan to explore different types of recipe adaptations while preserving recipe essential features such as cuisine style and essence simultaneously. We will also modify the rest of the recipe to the new changes to be reproducible.

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Section: Heterogeneous Data-handlingmentioning

confidence: 99%

“…We plan to test the model performances with different metrics including word mover's distance as a baseline metric. We also plan to use a distance metric proposed in (Morales-Garzón et al, 2020), which has demonstrated to work remarkably well with food data descriptions.…”

Section: Distance Metricsmentioning

confidence: 99%