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Optimizing text analytics and document automation with meta-algorithmic systems engineering

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dc.contributor.authorVillanueva, Arturo N., Jr., author
dc.contributor.authorSimske, Steven J., advisor
dc.contributor.authorHefner, Rick D., committee member
dc.contributor.authorKrishnaswamy, Nikhil, committee member
dc.contributor.authorMiller, Erika, committee member
dc.contributor.authorRoberts, Nicholas, committee member
dc.date.accessioned2023-08-28T10:29:11Z
dc.date.available2023-08-28T10:29:11Z
dc.date.issued2023
dc.description.abstractNatural language processing (NLP) has seen significant advances in recent years, but challenges remain in making algorithms both efficient and accurate. In this study, we examine three key areas of NLP and explore the potential of meta-algorithmics and functional analysis for improving analytic and machine learning performance and conclude with expansions for future research. The first area focuses on text classification for requirements engineering, where stakeholder requirements must be classified into appropriate categories for further processing. We investigate multiple combinations of algorithms and meta-algorithms to optimize the classification process, confirming the optimality of Naïve Bayes and highlighting a certain sensitivity to the Global Vectors (GloVe) word embeddings algorithm. The second area of focus is extractive summarization, which offers advantages to abstractive summarization due to its lossless nature. We propose a second-order meta-algorithm that uses existing algorithms and selects appropriate combinations to generate more effective summaries than any individual algorithm. The third area covers document ordering, where we propose techniques for generating an optimal reading order for use in learning, training, and content sequencing. We propose two main methods: one using document similarities and the other using entropy against topics generated through Latent Dirichlet Allocation (LDA).
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierVillanuevaJr_colostate_0053A_17799.pdf
dc.identifier.urihttps://hdl.handle.net/10217/236997
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectextractive summarization
dc.subjectmeta-algorithmics
dc.subjecttext classification
dc.subjectfunctional analysis
dc.subjectdocument ordering
dc.subjectnatural language processing
dc.titleOptimizing text analytics and document automation with meta-algorithmic systems engineering
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineSystems Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Engineering (Eng.D.)

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