Gorjan Popovski

Journal articlesshow recent

A Fine-Tuned Bidirectional Encoder Representations From Transformers for Food Named-Entity Recognition: Algorithm Development and Validation, JMIR (2021)
A Framework for Evaluating Personalized Ranking Systems by Fusing Different Evaluation Measures, BIG DATA RES (2021)
DietHub: Dietary Habits Analysis through Understanding the Content of Recipes, TRENDS FOOD SCI TECH (2020)
COVID-19 pandemic changes the food consumption patterns, TRENDS FOOD SCI TECH (2020)
A survey of named-entity recognition methods for food information extraction, IEEE ACCESS (2020)
FoodEx2vec: New foods’ representation for advanced food data analysis, FOOD CHEM TOXICOL (2020)
FoodBase corpus: a new resource of annotated food entities, DATABASE (2019)

Conference papersshow all

Books & Chaptersshow recent

Food Data Normalization Using Lexical and Semantic Similarities Heuristics, Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham., (2021)
FoodOntoMapV2: Food Concepts Normalization Across Food Ontologies, Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2019. Communications in Computer and Information Science, vol 1297. Springer, Cham, (2020)

FoodBase corpus: a new resource of annotated food entities

G. Popovski, B. Koroušić Seljak, T. Eftimov
Database: The Journal of Biological Databases and Curation, 2019
DOI: 10.1093/database/baz121

abstract:
The existence of annotated text corpora is essential for the development of public health services and tools based on natural language processing (NLP) and text mining. Recently organized biomedical NLP shared tasks have provided annotated corpora related to different biomedical entities such as genes, phenotypes, drugs, diseases and chemical entities. These are needed to develop named-entity recognition (NER) models that are used for extracting entities from text and finding their relations. However, to the best of our knowledge, there are limited annotated corpora that provide information about food entities despite food and dietary management being an essential public health issue. Hence, we developed a new annotated corpus of food entities, named FoodBase. It was constructed using recipes extracted from Allrecipes, which is currently the largest food-focused social network. The recipes were selected from five categories: ‘Appetizers and Snacks’, ‘Breakfast and Lunch’, ‘Dessert’, ‘Dinner’ and ‘Drinks’. Semantic tags used for annotating food entities were selected from the Hansard corpus. To extract and annotate food entities, we applied a rule-based food NER method called FoodIE. Since FoodIE provides a weakly annotated corpus, by manually evaluating the obtained results on 1000 recipes, we created a gold standard of FoodBase. It consists of 12 844 food entity annotations describing 2105 unique food entities. Additionally, we provided a weakly annotated corpus on an additional 21 790 recipes. It consists of 274 053 food entity annotations, 13 079 of which are unique. The FoodBase corpus is necessary for developing corpus-based NER models for food science, as a new benchmark dataset for machine learning tasks such as multi-class classification, multi-label classification and hierarchical multi-label classification. FoodBase can be used for detecting semantic differences/similarities between food concepts, and after all we believe that it will open a new path for learning food embedding space that can be used in predictive studies.