FoodChem: A food-chemical relation extraction model

TL;DR

FoodChem is a relation extraction model that identifies chemical components in food using fine-tuned transformer models on a novel biomedical dataset, achieving high accuracy in detecting food-chemical relations.

Contribution

The paper introduces FoodChem, a new relation extraction approach utilizing transformer models and a novel annotated dataset for identifying food-chemical relations.

Findings

BioBERT achieved a macro F1 score of 0.902.

The voting scheme improved dataset augmentation.

The model effectively identifies food-chemical contains relations.

Abstract

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.