Um método para descoberta de relacionamentos semânticos do tipo causa e efeito em sentenças de artigos científicos do domínio biomédico

• Ano de defesa: 2013
• Autor(a) principal: Scheicher, Ricardo Brigato
• Orientador(a): Ciferri, Ricardo Rodrigues
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• País: BR
• Palavras-chave em Português: Inteligência artificial; Relações semânticas; Rede semântica; Extração de informação; Mineração de textos; Domínio biomédico
• Palavras-chave em Inglês: Information extraction; Text mining; Semantic relations; Semantic networks; Biomedical domain; Sickle cell anemia
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/591

Recently, there is an enormous amount of scientific material written in textual format and published in electronic ways (paper on proceedings and articles on journals). In the biomedical field, researchers need to analyse a vast amount of information in order to update their knowledges, in order to get more precise diagnostics and propose more modern and effective treatments. The task of getting knowledge is extremely onerous and the manual process to annotate relationships and to propose novel hypothesis for treatments becomes very slow and error-prone. In this sense, as a result of this master s research it is proposed a method to extract cause and effect semantic relationships in sentences of scientific papers of the biomedical domain. The goal of this work is to propose and implements a solution for: (1) to extract terms from the biomedical domain (genes, proteins, chemical components, structures and anatomical processes, cell components and strutures, and treatmens), (2) to identify existing relationships on the texts, from the extracted terms, and (3) to suggest a knowledge network based on the relations of cause and effect . Over the approach using textual patterns, our proposed method had extracted semantic relations with a precision of 94,83 %, recall of 98,10 %, F-measure of 96,43 %.