OCTVis : ontology-based comparison of topic models
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Ge, Amon Dongfang
issued: 2019-04-24T14:38:15Z
issued: 2019
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: Topic modeling is a natural language processing (NLP) task that statistically identiﬁes topics from a set of texts. Evaluating results from topic modeling is diﬃcult in context and often requires domain experts. To facilitate evaluation of topic model results within communication between NLP researchers and domain experts, we present a visual comparison framework, OCTVis, to explore results from two topic models mapped against a domain ontology. The design of OCTVis is based on detailed and abstracted data and task models. We support high-level topic model comparison by mapping topics onto ontology concepts and incorporating topic alignment visualizations. For in-depth exploration of the dataset, display of per-document topic distributions and buddy plots allow comparison of topics, texts, and shared keywords at the document level. Case studies with medical domain experts using healthcare texts indicate that our framework enhances qualitative evaluation of topic models and provide a clearer understanding of how topic models can be improved.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/69898
note: OCTVis: Ontology-based Comparisonof Topic ModelsbyAmon Dongfang GeB.Sc., University of British Columbia, 2016A 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)April 2019c© Amon Dongfang Ge, 2019The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, a thesisentitled:OCTVis: Ontology-based Comparison of Topic Modelssubmitted by Amon Dongfang Ge in partial fulfillment of the require-ments for the degree of Master of Science in Computer Science.Examining Committee:Giuseppe Carenini, Computer ScienceSupervisorDongwook Yoon, Computer ScienceSupervisory Committee MemberiiAbstractTopic modeling is a natural language processing (NLP) task that statis-tically identifies topics from a set of texts. Evaluating results from topicmodeling is difficult in context and often requires domain experts. To facil-itate evaluation of topic model results within communication between NLPresearchers and domain experts, we present a visual comparison framework,OCTVis, to explore results from two topic models mapped against a domainontology. The design of OCTVis is based on detailed and abstracted dataand task models. We support high-level topic model comparison by mappingtopics onto ontology concepts and incorporating topic alignment visualiza-tions. For in-depth exploration of the dataset, display of per-documenttopic distributions and buddy plots allow comparison of topics, texts, andshared keywords at the document level. Case studies with medical domainexperts using healthcare texts indicate that our framework enhances qual-itative evaluation of topic models and provide a clearer understanding ofhow topic models can be improved.iiiLay SummaryTopic modeling is a statistical approach that summarizes collections of textinto topics being discussed. While topi…
has type: Thesis/Dissertation
date issued: 2019-05
is shown at: 10.14288/1.0378428
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
type: Text
identifier URI: http://hdl.handle.net/2429/69898