Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks

One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in scientific texts were automatically classified. It...

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Autores principales:	Rohit Bhuvaneshwar Mishra, Hongbing Jiang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	discourse analysis problem–solution pattern automatic classification machine learning classifiers deep neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/739b91b796d549259a112639439ed0b9
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spelling	oai:doaj.org-article:739b91b796d549259a112639439ed0b92021-11-11T15:05:00ZClassification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks10.3390/app112199972076-3417https://doaj.org/article/739b91b796d549259a112639439ed0b92021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9997https://doaj.org/toc/2076-3417One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in scientific texts were automatically classified. It would help in knowledge mining, idea generation, and information classification from scientific texts. It would also help to compare scientific papers and automatically generate review articles in a given field. However, computational research on problem–solution patterns has been scarce. The linguistic analysis, instructional-design research, theory, and empirical methods have not paid enough attention to the study of problem–solution patterns. This paper tries to solve this issue by applying the computational techniques of machine learning classifiers and neural networks to a set of features to intelligently classify a problem phrase from a non-problem phrase and a solution phrase from a non-solution phrase. Our analysis shows that deep learning networks outperform machine learning classifiers. Our best model was able to classify a problem phrase from a non-problem phrase with an accuracy of 90.0% and a solution phrase from a non-solution phrase with an accuracy of 86.0%.Rohit Bhuvaneshwar MishraHongbing JiangMDPI AGarticlediscourse analysisproblem–solution patternautomatic classificationmachine learning classifiersdeep neural networksTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9997, p 9997 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	discourse analysis problem–solution pattern automatic classification machine learning classifiers deep neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	discourse analysis problem–solution pattern automatic classification machine learning classifiers deep neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Rohit Bhuvaneshwar Mishra Hongbing Jiang Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks
description	One of the central aspects of science is systematic problem-solving. Therefore, problem and solution statements are an integral component of the scientific discourse. The scientific analysis would be more successful if the problem–solution claims in scientific texts were automatically classified. It would help in knowledge mining, idea generation, and information classification from scientific texts. It would also help to compare scientific papers and automatically generate review articles in a given field. However, computational research on problem–solution patterns has been scarce. The linguistic analysis, instructional-design research, theory, and empirical methods have not paid enough attention to the study of problem–solution patterns. This paper tries to solve this issue by applying the computational techniques of machine learning classifiers and neural networks to a set of features to intelligently classify a problem phrase from a non-problem phrase and a solution phrase from a non-solution phrase. Our analysis shows that deep learning networks outperform machine learning classifiers. Our best model was able to classify a problem phrase from a non-problem phrase with an accuracy of 90.0% and a solution phrase from a non-solution phrase with an accuracy of 86.0%.
format	article
author	Rohit Bhuvaneshwar Mishra Hongbing Jiang
author_facet	Rohit Bhuvaneshwar Mishra Hongbing Jiang
author_sort	Rohit Bhuvaneshwar Mishra
title	Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks
title_short	Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks
title_full	Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks
title_fullStr	Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks
title_full_unstemmed	Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks
title_sort	classification of problem and solution strings in scientific texts: evaluation of the effectiveness of machine learning classifiers and deep neural networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/739b91b796d549259a112639439ed0b9
work_keys_str_mv	AT rohitbhuvaneshwarmishra classificationofproblemandsolutionstringsinscientifictextsevaluationoftheeffectivenessofmachinelearningclassifiersanddeepneuralnetworks AT hongbingjiang classificationofproblemandsolutionstringsinscientifictextsevaluationoftheeffectivenessofmachinelearningclassifiersanddeepneuralnetworks
_version_	1718437150240800768

Classification of Problem and Solution Strings in Scientific Texts: Evaluation of the Effectiveness of Machine Learning Classifiers and Deep Neural Networks

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