A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation

Abstract

Background: Recently, food science has been garnering a lot of attention. There are many open research questions on food interactions, as one of the main environmental factors, with other health-related entities such as diseases, treatments, and drugs. In the last 2 decades, a large amount of work has been done in natural language processing and machine learning to enable biomedical information extraction. However, machine learning in food science domains remains inadequately resourced, which brings to attention the problem of developing methods for food information extraction. There are only few food semantic resources and few rule-based methods for food information extraction, which often depend on some external resources. However, an annotated corpus with food entities along with their normalization was published in 2019 by using several food semantic resources.

Objective: In this study, we investigated how the recently published bidirectional encoder representations from transformers (BERT) model, which provides state-of-the-art results in information extraction, can be fine-tuned for food information extraction.

Methods: We introduce FoodNER, which is a collection of corpus-based food named-entity recognition methods. It consists of 15 different models obtained by fine-tuning 3 pretrained BERT models on 5 groups of semantic resources: food versus nonfood entity, 2 subsets of Hansard food semantic tags, FoodOn semantic tags, and Systematized Nomenclature of Medicine Clinical Terms food semantic tags.

Results: All BERT models provided very promising results with 93.30% to 94.31% macro F1 scores in the task of distinguishing food versus nonfood entity, which represents the new state-of-the-art technology in food information extraction. Considering the tasks where semantic tags are predicted, all BERT models obtained very promising results once again, with their macro F1 scores ranging from 73.39% to 78.96%.

Conclusions: FoodNER can be used to extract and annotate food entities in 5 different tasks: food versus nonfood entities and distinguishing food entities on the level of food groups by using the closest Hansard semantic tags, the parent Hansard semantic tags, the FoodOn semantic tags, or the Systematized Nomenclature of Medicine Clinical Terms semantic tags.

Keywords: BERT; bidirectional encoder representations from transformers; fine-tuning BERT; food information extraction; information extraction; machine learning; named-entity recognition; natural language processing; semantic annotation.

Figure 1

Recipe example.

Figure 2

Food named-entity recognition flowchart. BERT: bidirectional encoder representations from transformers; NER: named-entity recognition; SNOMED CT: Systematized Nomenclature of Medicine Clinical Terms.

Figure 3

An example of food entities available from one recipe that are present in the training data set. The entities are annotated using Hansard parent, Hansard closest, FoodOn, Systematized Nomenclature of Medicine Clinical Terms, and OntoFood (not studied in this paper) semantic tags.

Figure 4

Training and validation loss per fine-tuning epoch for the bio bidirectional encoder representations from transformers large model on the Hansard parent data set.

Figure 5

Macro F1 scores for all considered models for the food versus nonfood entity task. Each macro F1 score is obtained by using stratified k-fold cross-validation (k=5). Underlined values are best per subtable, while the bold value is the best from the whole table. BERT: bidirectional encoder representations from transformers; BiLSTM-CRF: bidirectional long short-term memory conditional random field; BuTTER: bidirectional long short-term memory for food named-entity recognition; NER: named-entity recognition.

Figure 6

Boxplots of macro F1 scores obtained by using stratified five-fold cross-validation for all considered models for the binary food classification task. BERT: bidirectional encoder representations from transformers; BiLSTM-CRF: bidirectional long short-term memory conditional random field.

Figure 7

Food named-entity recognition integration in FoodViz.