# FoodChem: A food-chemical relation extraction model

arXiv: https://arxiv.org/abs/2110.02019 · HTML: https://mlsanity.com/paper/2110.02019/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [Knowledge-guided Convolutional Networks for Chemical-Disease Relation Extraction](https://mlsanity.com/paper/1912.10590/) (2019)
- [Chemical-induced Disease Relation Extraction with Dependency Information and Prior Knowledge](https://mlsanity.com/paper/2001.00295/) (2020)

## Built on by (most cited first, 2 of 2)

- [DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations](https://mlsanity.com/paper/2409.19581/) (2024)
- [A framework for recipe data structure with applications for culinary and nutritional insights](https://mlsanity.com/paper/2609.22099/) (2026)

## Abstract (arXiv)

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. The RE task is treated as a binary classification problem, aimed at identifying whether the contains relation exists between a food-chemical entity pair. This is accomplished by fine-tuning BERT, BioBERT and RoBERTa transformer models. For evaluation purposes, a novel dataset with annotated contains relations in food-chemical entity pairs is generated, in a golden and silver version. The models are integrated into a voting scheme in order to produce the silver version of the dataset which we use for augmenting the individual models, while the manually annotated golden version is used for their evaluation. Out of the three evaluated models, the BioBERT model achieves the best results, with a macro averaged F1 score of 0.902 in the unbalanced augmentation setting.