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

Andrea Morales-Garzón, Juan Gómez-Romero, Maria J. Martin-Bautista

Abstract
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.
Anthology ID:
2021.eacl-srw.20
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
148–154
Language:
URL:
https://aclanthology.org/2021.eacl-srw.20
DOI:
10.18653/v1/2021.eacl-srw.20
Bibkey:
Cite (ACL):
Andrea Morales-Garzón, Juan Gómez-Romero, and Maria J. Martin-Bautista. 2021. Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 148–154, Online. Association for Computational Linguistics.
Cite (Informal):
Semantic-aware transformation of short texts using word embeddings: An application in the Food Computing domain (Morales-Garzón et al., EACL 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.eacl-srw.20.pdf
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