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Open AccessArticle

Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks

by Wiem Chebil, Mohammad Wedyan, Moutaz Alazab, Ryan Alturki and Omar Elshaweesh

Information 2023, 14(5), 272; https://doi.org/10.3390/info14050272 - 3 May 2023

Cited by 18 | Viewed by 5375

This research proposes a new approach to improve information retrieval systems based on a multinomial naive Bayes classifier (MNBC), Bayesian networks (BNs), and a multi-terminology which includes MeSH thesaurus (Medical Subject Headings) and SNOMED CT (Systematized Nomenclature of Medicine of Clinical Terms). Our approach, which is entitled improving semantic information retrieval (IMSIR), extracts and disambiguates concepts and retrieves documents. Relevant concepts of ambiguous terms were selected using probability measures and biomedical terminologies. Concepts are also extracted using an MNBC. The UMLS (Unified Medical Language System) thesaurus was then used to filter and rank concepts. Finally, we exploited a Bayesian network to match documents and queries using a conceptual representation. Our main contribution in this paper is to combine a supervised method (MNBC) and an unsupervised method (BN) to extract concepts from documents and queries. We also propose filtering the extracted concepts in order to keep relevant ones. Experiments of IMSIR using the two corpora, the OHSUMED corpus and the Clinical Trial (CT) corpus, were interesting because their results outperformed those of the baseline: the P@50 improvement rate was +36.5% over the baseline when the CT corpus was used. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Applications)

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15 pages, 1886 KB

Open AccessArticle

A Novel Multi-View Ensemble Learning Architecture to Improve the Structured Text Classification

by Carlos Adriano Gonçalves, Adrián Seara Vieira, Célia Talma Gonçalves, Rui Camacho, Eva Lorenzo Iglesias and Lourdes Borrajo Diz

Information 2022, 13(6), 283; https://doi.org/10.3390/info13060283 - 1 Jun 2022

Cited by 11 | Viewed by 5482

Abstract

Multi-view ensemble learning exploits the information of data views. To test its efficiency for full text classification, a technique has been implemented where the views correspond to the document sections. For classification and prediction, we use a stacking generalization based on the idea that different learning algorithms provide complementary explanations of the data. The present study implements the stacking approach using support vector machine algorithms as the baseline and a C4.5 implementation as the meta-learner. Views are created with OHSUMED biomedical full text documents. Experimental results lead to the sustained conclusion that the application of multi-view techniques to full texts significantly improves the task of text classification, providing a significant contribution for the biomedical text mining research. We also have evidence to conclude that enriched datasets with text from certain sections are better than using only titles and abstracts. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Natural Language Processing)

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13 pages, 291 KB

Open AccessArticle

Classification of Full Text Biomedical Documents: Sections Importance Assessment

by Carlos Adriano Oliveira Gonçalves, Rui Camacho, Célia Talma Gonçalves, Adrián Seara Vieira, Lourdes Borrajo Diz and Eva Lorenzo Iglesias

Appl. Sci. 2021, 11(6), 2674; https://doi.org/10.3390/app11062674 - 17 Mar 2021

Cited by 6 | Viewed by 3970

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. Full article

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