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Recognizing Scientific Artifacts in Biomedical Literature
Today’s search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3623603/ https://www.ncbi.nlm.nih.gov/pubmed/23645987 http://dx.doi.org/10.4137/BII.S11572 |
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author | Groza, Tudor Hassanzadeh, Hamed Hunter, Jane |
author_facet | Groza, Tudor Hassanzadeh, Hamed Hunter, Jane |
author_sort | Groza, Tudor |
collection | PubMed |
description | Today’s search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central themes within a domain or to detect gaps between accepted knowledge and newly emerging knowledge as a means for tracking the evolution of hypotheses from incipient phases to maturity or decline. We present a hybrid Machine Learning approach using an ensemble of four classifiers, for recognizing scientific artifacts (ie, hypotheses, background, motivation, objectives, and findings) within biomedical research publications, as a precursory step to the general goal of automatically creating argumentative discourse networks that span across multiple publications. The performance achieved by the classifiers ranges from 15.30% to 78.39%, subject to the target class. The set of features used for classification has led to promising results. Furthermore, their use strictly in a local, publication scope, ie, without aggregating corpus-wide statistics, increases the versatility of the ensemble of classifiers and enables its direct applicability without the necessity of re-training. |
format | Online Article Text |
id | pubmed-3623603 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
spelling | pubmed-36236032013-05-03 Recognizing Scientific Artifacts in Biomedical Literature Groza, Tudor Hassanzadeh, Hamed Hunter, Jane Biomed Inform Insights Original Research Today’s search engines and digital libraries offer little or no support for discovering those scientific artifacts (hypotheses, supporting/contradicting statements, or findings) that form the core of scientific written communication. Consequently, we currently have no means of identifying central themes within a domain or to detect gaps between accepted knowledge and newly emerging knowledge as a means for tracking the evolution of hypotheses from incipient phases to maturity or decline. We present a hybrid Machine Learning approach using an ensemble of four classifiers, for recognizing scientific artifacts (ie, hypotheses, background, motivation, objectives, and findings) within biomedical research publications, as a precursory step to the general goal of automatically creating argumentative discourse networks that span across multiple publications. The performance achieved by the classifiers ranges from 15.30% to 78.39%, subject to the target class. The set of features used for classification has led to promising results. Furthermore, their use strictly in a local, publication scope, ie, without aggregating corpus-wide statistics, increases the versatility of the ensemble of classifiers and enables its direct applicability without the necessity of re-training. Libertas Academica 2013-04-02 /pmc/articles/PMC3623603/ /pubmed/23645987 http://dx.doi.org/10.4137/BII.S11572 Text en © 2013 the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited. |
spellingShingle | Original Research Groza, Tudor Hassanzadeh, Hamed Hunter, Jane Recognizing Scientific Artifacts in Biomedical Literature |
title | Recognizing Scientific Artifacts in Biomedical Literature |
title_full | Recognizing Scientific Artifacts in Biomedical Literature |
title_fullStr | Recognizing Scientific Artifacts in Biomedical Literature |
title_full_unstemmed | Recognizing Scientific Artifacts in Biomedical Literature |
title_short | Recognizing Scientific Artifacts in Biomedical Literature |
title_sort | recognizing scientific artifacts in biomedical literature |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3623603/ https://www.ncbi.nlm.nih.gov/pubmed/23645987 http://dx.doi.org/10.4137/BII.S11572 |
work_keys_str_mv | AT grozatudor recognizingscientificartifactsinbiomedicalliterature AT hassanzadehhamed recognizingscientificartifactsinbiomedicalliterature AT hunterjane recognizingscientificartifactsinbiomedicalliterature |