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Found 3 papers, 1 papers with code
SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN
The accelerating growth of big data in the biomedical domain, with an endless amount of electronic health records and more than 30 million citations and abstracts in PubMed, introduces the need for automatic structuring of textual biomedical data.
Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets
From the list of ingredients, domain-specific embeddings are created using the same embedding space for all recipes - one ingredient dataset is generated.
FoodChem: A food-chemical relation extraction model
In this paper, we present FoodChem, a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities, based on textual information provided in biomedical peer-reviewed scientific literature.
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