This paper is in the following e-collection/theme issue:

New Methods (944) Natural Language Processing (693) Food Sciences and Nutrition Engineering (54) Ontologies, Classifications, and Coding (239) Machine Learning (1465) Data Science (270)

Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28229, first published .
A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation

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

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

Authors of this article:

Riste Stojanov1 Author Orcid Image ;   Gorjan Popovski2, 3 Author Orcid Image ;   Gjorgjina Cenikj2, 3 Author Orcid Image ;   Barbara Koroušić Seljak2 Author Orcid Image ;   Tome Eftimov2 Author Orcid Image
Article Authors Cited by (14) Tweetations (7) Metrics

Journals

  1. Emara Y, Koroušić Seljak B, Gibney E, Popovski G, Pravst I, Fantke P. Workflow for building interoperable food and nutrition security (FNS) data platforms. Trends in Food Science & Technology 2022;123:310 View
  2. Ispirova G, Cenikj G, Ogrinc M, Valenčič E, Stojanov R, Korošec P, Cavalli E, Koroušić Seljak B, Eftimov T. CafeteriaFCD Corpus: Food Consumption Data Annotated with Regard to Different Food Semantic Resources. Foods 2022;11(17):2684 View
  3. Komariah K, Purnomo A, Satriawan A, Hasanuddin M, Setianingsih C, Sin B. SMPT: A Semi-Supervised Multi-Model Prediction Technique for Food Ingredient Named Entity Recognition (FINER) Dataset Construction. Informatics 2023;10(1):10 View
  4. Komariah K, Sin B. Enhancing Food Ingredient Named-Entity Recognition with Recurrent Network-Based Ensemble (RNE) Model. Applied Sciences 2022;12(20):10310 View
  5. Feher D, Ibrahim F, Cheng Z, Schlegel V, Maidment T, Bagshaw J, Batista-Navarro R. Few-shot entity linking of food names. Information Processing & Management 2023;60(5):103463 View
  6. Chen Y, Li X, Li A, Li Y, Yang X, Lin Z, Yu S, Tang X. A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study. JMIR Formative Research 2023;7:e47434 View
  7. Akujuobi U, Liu S, Besold T. Revisiting named entity recognition in food computing: enhancing performance and robustness. Artificial Intelligence Review 2024;57(9) View
  8. Ogrinc M, Koroušić Seljak B, Eftimov T. Zero-shot evaluation of ChatGPT for food named-entity recognition and linking. Frontiers in Nutrition 2024;11 View
  9. Menichetti G, Barabási A, Loscalzo J. Decoding the Foodome: Molecular Networks Connecting Diet and Health. Annual Review of Nutrition 2024;44(1):257 View
  10. Chen S, Yu J, Chamouni S, Wang Y, Li Y. Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions. BMC Medicine 2024;22(1) View

Books/Policy Documents

  1. Karaca Y, Zhang Y, Dursun A, Wang S. Multi-Chaos, Fractal and Multi-Fractional Artificial Intelligence of Different Complex Systems. View