Authors:
Gorjan Popovski
1
;
Stefan Kochev
1
;
Barbara Koroušić Seljak
2
and
Tome Eftimov
2
Affiliations:
1
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Rugjer Boshkovikj 16, 1000 Skopje and Macedonia
;
2
Computer Systems Department, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana and Slovenia
Keyword(s):
Information Extraction, Rule-based Named-entity Recognition, Food-entity Recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Natural Language Processing
;
Pattern Recognition
;
Symbolic Systems
Abstract:
The application of Natural Language Processing (NLP) methods and resources to biomedical textual data has received growing attention over the past years. Previously organized biomedical NLP-shared tasks (such as, for example, BioNLP Shared Tasks) are related to extracting different biomedical entities (like genes, phenotypes, drugs, diseases, chemical entities) and finding relations between them. However, to the best of our knowledge there are limited NLP methods that can be used for information extraction of entities related to food concepts. For this reason, to extract food entities from unstructured textual data, we propose a rule-based named-entity recognition method for food information extraction, called FoodIE. It is comprised of a small number of rules based on computational linguistics and semantic information that describe the food entities. Experimental results from the evaluation performed using two different datasets showed that very promising results can be achieved. Th
e proposed method achieved 97% precision, 94% recall, and 96% F1 score.
(More)