Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/35717
DC FieldValueLanguage
dc.contributor.advisorRibeiro, Bernardete Martins-
dc.contributor.authorLourenço, Mariana Rodrigues-
dc.date.accessioned2017-01-13T16:07:08Z-
dc.date.available2017-01-13T16:07:08Z-
dc.date.issued2015-07-17por
dc.identifier.urihttp://hdl.handle.net/10316/35717-
dc.descriptionDissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbrapor
dc.description.abstractWe live in an era where information over ows. Yet, for this information to become knowledge, it has to be given meaning. This thesis focuses on a machine learning approach that evolved from probabilistic graphical models, which automatically extracts knowledge from vast amounts of data by assigning themes to documents: topic modeling. Topic models are an emergent technique used for both descriptive and predictive tasks. As a result, it was soon extended to other goals that do not only model topics, but also target variables. This work presents a supervised topic model that is able to learn from crowds. That is, we consider the case where the label set of the data was provided by multiple annotators. In the multi-annotator setting, the ground truth labels need to be modeled from several noisy versions of them given by the di erent annotators. To address this sort of problems, it is often assumed that all labelers are equally reliable through the use of voting techniques, which was proven to be an unrealistic conjecture. On the contrary, the proposed model takes into account the di erent levels of expertise and biases of annotators, by jointly modeling them together with the topics and the true labels. In order to make this process computationally tractable, a variational inference algorithm was developed, which provides an e cient approximate inference method. We nalize by showing how general supervised topic models can be used to predict demand in special events by correlating internet search query data with real measurements of transport usage, thus, motivating the usage of the topic models in real-world applications.por
dc.language.isoengpor
dc.rightsopenAccesspor
dc.subjectAnnotatorspor
dc.titleSupervised Topic Models with Multiple Annotatorspor
dc.typemasterThesispor
degois.publication.locationCoimbrapor
degois.publication.titleSupervised Topic Models with Multiple Annotatorspor
dc.identifier.tid201537931por
thesis.degree.grantorUniversidade de Coimbrapor
thesis.degree.nameMestrado em Engenharia Informáticapor
uc.degree.grantorUnit0501 - Faculdade de Ciências e Tecnologiapor
uc.controloAutoridadeSim-
item.languageiso639-1en-
item.fulltextCom Texto completo-
item.grantfulltextopen-
crisitem.advisor.deptFaculdade de Ciências e Tecnologia, Universidade de Coimbra-
crisitem.advisor.parentdeptUniversidade de Coimbra-
crisitem.advisor.researchunitCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA-
crisitem.advisor.orcid0000-0002-9770-7672-
Appears in Collections:UC - Dissertações de Mestrado
FCTUC Eng.Informática - Teses de Mestrado
Files in This Item:
File Description SizeFormat
Supervised Topic Models with Multiple Annotators.pdf7.55 MBAdobe PDFView/Open
Show simple item record

Page view(s) 20

543
checked on Oct 14, 2020

Download(s)

171
checked on Oct 14, 2020

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.