Neural multimodal topic modeling : a comprehensive evaluation
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: González Pizarro, Felipe
issued: 2023-08-14T19:31:37Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This thesis presents the first systematic and comprehensive evaluation of multimodal topic modeling of documents containing both text and images. In the process, we propose three novel topic modeling solutions and two novel evaluation metrics. Moreover, we focus on one of our models and explore additional techniques to improve the quality of topics, such as incorporating external knowledge. Overall, our evaluation on an unprecedented rich and diverse collection of datasets indicates that all of our models generate coherent and diverse topics. Nevertheless, the extent to which one method outperforms the other depends on the metrics and dataset combinations, which suggests further exploration of combined approaches in the future.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85455
note: Neural Multimodal Topic Modeling: AComprehensive EvaluationbyFelipe Gonza´lez Pizarro,MSc. in Computer Science,Universidad Te´cnica Federico Santa Mar´ıa, 2021BSc. in Computer Science, Universidad Te´cnica Federico Santa Mar´ıa, 2018A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Computer Science)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2023© Felipe Gonza´lez Pizarro, 2023The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Neural Multimodal Topic Modeling: A Comprehensive Evaluationsubmitted by Felipe Gonza´lez Pizarro in partial fulfillment of the re-quirements for the degree of Master of Science in Computer Science.Examining Committee:Giuseppe Carenini, Professor, Department of Computer Science, UBCSupervisorVered Shwartz, Assistant Professor, Department of Computer Science, UBCSupervisory Committee MemberiiAbstractNeural topic models can successfully find coherent and diverse topics in tex-tual data. However, they are limited in dealing with multimodal datasets(e.g., images and text). This thesis presents the first systematic and compre-hensive evaluation of multimodal topic modeling of documents containingboth text and images. In the process, we propose three novel topic model-ing solutions and two novel evaluation metrics. Moreover, we focus on oneof our models and explore additional techniques to improve the quality oftopics, such as incorporating external knowledge. Overall, our evaluationon an unprecedented rich and diverse collection of datasets indicates thatall of our models generate coherent and diverse topics. Nevertheless, theextent to which one method outperforms the other depends on the metricsand dataset combinations, which suggests further exploration of combinedapproaches in the future.iiiLay SummaryThe constant increase in the volume of d…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0435191
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Carenini, Giuseppe
type: Text
identifier URI: http://hdl.handle.net/2429/85455