ArticlePloS one2017
A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 165 citations in OpenAlex.
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- Large language model powered knowledge graph construction for mental health exploration.Nature communications · 2025Article
- Knowledge discovery of diseases symptoms and rehabilitation measures in Q&A communities.Scientific reports · 2025Article
- SciLinker: a large-scale text mining framework for mapping associations among biological entities.Frontiers in artificial intelligence · 2025Article
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- Zero-shot evaluation of ChatGPT for food named-entity recognition and linking.Frontiers in nutrition · 2024Article
- BERT-based tourism named entity recognition: making use of social media for travel recommendations.PeerJ. Computer science · 2023Article
- CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources.Database : the journal of biological databases and curation · 2022Article
- Disease- and Drug-Related Knowledge Extraction for Health Management from Online Health Communities Based on BERT-BiGRU-ATT.International journal of environmental research and public health · 2022Article
- Entity relation extraction in the medical domain: based on data augmentation.Annals of translational medicine · 2022Article
- CafeteriaFCD Corpus: Food Consumption Data Annotated with Regard to Different Food Semantic Resources.Foods (Basel, Switzerland) · 2022Article
- A Smart Mobile App to Simplify Medical Documents and Improve Health Literacy: System Design and Feasibility Validation.JMIR formative research · 2022Article
- A Fine-Tuned Bidirectional Encoder Representations From Transformers Model for Food Named-Entity Recognition: Algorithm Development and Validation.Journal of medical Internet research · 2021Article
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- RecipeDB: a resource for exploring recipes.Database : the journal of biological databases and curation · 2020Article
- Named Entity Recognition and Relation Detection for Biomedical Information Extraction.Frontiers in cell and developmental biology · 2020Review
- Food Composition at Present: New Challenges.Nutrients · 2019Review
- FoodBase corpus: a new resource of annotated food entities.Database : the journal of biological databases and curation · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evidence-based dietary information represented as unstructured text is a crucial information that needs to be accessed in order to help dietitians follow the new knowledge arrives daily with newly published scientific reports. Different named-entity recognition (NER) methods have been introduced previously to extract useful information from the biomedical literature. They are focused on, for example extracting gene mentions, proteins mentions, relationships between genes and proteins, chemical concepts and relationships between drugs and diseases. In this paper, we present a novel NER method, called drNER, for knowledge extraction of evidence-based dietary information. To the best of our knowledge this is the first attempt at extracting dietary concepts. DrNER is a rule-based NER that consists of two phases. The first one involves the detection and determination of the entities mention, and the second one involves the selection and extraction of the entities. We evaluate the method by using text corpora from heterogeneous sources, including text from several scientifically validated web sites and text from scientific publications. Evaluation of the method showed that drNER gives good results and can be used for knowledge extraction of evidence-based dietary recommendations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.