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Journal article Open Access Scopus Source The journal / series is indexed in Scopus (SCImago list). The article itself has not been verified. 6 Citations Quartil Q2

Classification of Full Text Biomedical Documents: Sections Importance Assessment

Carlos A. Gonçalves , Rui Camacho , Célia Talma Gonçalves , Adrián Seara Vieira , Lourdes Borrajo Diz , Eva Lorenzo Iglesias
ISLA — Instituto Politécnico de Gestão e Tecnologia
Applied Sciences, Volume 11, Issue 6, 17 03 2021, pp. 2674–2674
DOI: 10.3390/app11062674

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Abstract
The exponential growth of documents in the web makes it very hard for researchers to be aware of the relevant work being done within the scientific community. The task of efficiently retrieving information has therefore become an important research topic. The objective of this study is to test how the efficiency of the text classification changes if different weights are previously assigned to the sections that compose the documents. The proposal takes into account the place (section) where terms are located in the document, and each section has a weight that can be modified depending on the corpus. To carry out the study, an extended version of the OHSUMED corpus with full documents have been created. Through the use of WEKA, we compared the use of abstracts only with that of full texts, as well as the use of section weighing combinations to assess their significance in the scientific article classification process using the SMO (Sequential Minimal Optimization), the WEKA Support Vector Machine (SVM) algorithm implementation. The experimental results show that the proposed combinations of the preprocessing techniques and feature selection achieve promising results for the task of full text scientific document classification. We also have evidence to conclude that enriched datasets with text from certain sections achieve better results than using only titles and abstracts.

Keywords

#feature selection #text preprocessing #support vector machine #sequential minimal optimization #Computer science #Preprocessor #Information Retrieval #Task (project management) #Section (typography) #Selection (genetic algorithm)

Bibliographic Details

Publication Type Journal article
Publication Year 2021
Journal / Conference / Work Applied Sciences
ISSN 2076-3417
Volume / Issue Vol. 11 (6)
Pagination pp. 2674–2674
Quartile (SCImago) Q2
Scopus Indexing Indexed source (via SCImago)

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