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

Stojanov, Riste; Popovski, Gorjan; Cenikj, Gjorgjina; Koroušić Seljak, Barbara; Eftimov, Tome

doi:10.2196/28229

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

Journal

Journal of Medical Internet Research

Date Issued

2021

Author(s)

Popovski, Gorjan

Cenikj, Gjorgjina

Koroušić Seljak, Barbara

Eftimov, Tome

DOI

10.2196/28229

Abstract

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.

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