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CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources
Gjorgjina Cenikj1,2, Eva Valenčič1,2,3,4, Gordana Ispirova1,2
1Department of Computer Systems, Jožef Stefan Institute, Jamova cesta 39, Ljubljana 1000, Slovenia.
Database : the Journal of Biological Databases and Curation
|December 16, 2022
Summary
Researchers created the first annotated corpus (CafeteriaSA) for food and nutrition data. This resource aids natural language processing (NLP) for extracting vital food information from scientific texts.
Area of Science:
- Food science and nutrition
- Biomedical informatics
- Natural Language Processing (NLP)
Background:
- Significant advancements in predictive modeling for human and environmental health exist.
- Biomedical vocabularies and standards facilitate health information understanding and data analysis.
- A notable gap exists in semantic resources for the food and nutrition domain and their interconnections.
Purpose of the Study:
- To address the lack of semantic resources in the food and nutrition domain.
- To develop an annotated corpus for food information extraction.
- To support the advancement of NLP methods in food science.
Main Methods:
- Development of the CafeteriaSA corpus, an annotated collection of 500 scientific abstracts.
- Annotation of 6407 food entities using Hansard taxonomy.
- Annotation of 4299 food entities using FoodOn and 3623 using SNOMED-CT.
Main Results:
- Creation of the first comprehensive annotated corpus for food and nutrition research (CafeteriaSA).
- Extensive annotation of food entities across multiple established taxonomies.
- Establishment of a valuable resource for NLP development in food science.
Conclusions:
- The CafeteriaSA corpus is a foundational resource for advancing NLP in food and nutrition.
- This corpus will enable more sophisticated extraction of food-related information from scientific literature.
- Facilitates future research at the intersection of food science, health, and informatics.
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