Boosting biomedical document classification through the use of domain entity recognizers and semantic ontologies for document representation: The case of gluten bibliome
| Autor: | Gilberto Igrejas, Tânia Ferreira, Anália Lourenço, Florentino Fdez-Riverola, Martín Pérez-Pérez |
|---|---|
| Přispěvatelé: | Universidade do Minho |
| Rok vydání: | 2022 |
| Předmět: |
Text corpus
Boosting (machine learning) Word embedding Computer science Cognitive Neuroscience Gluten bibliome 02 engineering and technology Ontology (information science) computer.software_genre Semi-automatic curation Document classification 03 medical and health sciences Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Relevance (information retrieval) 1203 Ciencia de Los Ordenadores 030304 developmental biology 0303 health sciences Science & Technology Information retrieval Bibliome Ontology-based representation Computer Science Applications Workflow 1203.04 Inteligencia Artificial 2499 Otras Especialidades Biológicas 020201 artificial intelligence & image processing Literature mining computer |
| Zdroj: | Neurocomputing. 484:223-237 |
| ISSN: | 0925-2312 |
| Popis: | "Available online 11 November 2021" The increasing number of scientific research documents published keeps growing at an unprecedented rate, making it increasingly difficult to access practical information within a target domain. This situation is motivating a growing interest in applying text mining techniques for the automatic processing of text resources to structure the information that helps researchers to find information of interest and infer knowledge of practical use. However, the automatic processing of research documents requires the previous existence of large, manually annotated text corpora to develop robust and accurate text mining processing methods and machine learning models. In this context, semi-automatic extraction techniques based on structured data and state-of-the-art biomedical tools appear to have significant potential to enhance curator productivity and reduce the costs of document curation. In this line, this work proposes a semi-automatic machine learning workflow and a NER+Ontology boosting technique for the automatic classification of biomedical literature. The practical relevance of the proposed approach has been proven in the curation of 4,115 gluten-related documents extracted from PubMed and contrasted against the word embedding alternative. Comparing the results of the experiments, the proposed NER+Ontology technique is an effective alternative to other state-of-the-art document representation techniques to process the existing biomedical literature. This work was supported by: the Associate Laboratory for Green Chemistry - LAQV financed by the Portuguese Foundation for Science and Technology (FCT/MCTES) Ref. UID/QUI/50006/2020; the Portuguese Foundation for Science and Technology (FCT/MCTES) under the scope of the strategic funding of UIDB/04469/2020 unit and BioTecNorte operation funded by the European Regional Development Fund (ERDF) under the scope of Norte2020— Programa Operacional Regional do Norte. Ref. NORTE-01-0145-FEDER-000004; the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group, the “Centro singular de investigación de Galicia” (accreditation 2019-2022) funded by the European Regional Development Fund (ERDF)-Ref. ED431G2019/06. The authors also acknowledge the postdoctoral fellowship [ED481B-2019-032] of Martín Pérez-Pérez, funded by Xunta de Galicia. info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect